Monthly Labor Review • April 2007 

Establishment Wage Differentials

Julia I. Lane

Laurie A. Salmon

James R. Spletzer Economists have long known that 

individual wages depend on a combination of employee and employer 

characteristics, as well as the interaction of 

the two. Although understanding establishment wage differentials is important for labor economics and theories of the firm, little 

is known about the magnitude of these wage 

differentials. Primarily this stems from the 

lack of microdata linking individuals to the 

establishments where they work, but also it 

reflects the technical difficulties associated 

with separating out employee and employer 

effects. This article provides new findings using microdata from the Occupational Employment Statistics program at the Bureau 

of Labor Statistics that permit both of these 

issues to be addressed. The data used for the 

research contain information from more 

than half a million establishments, in all 

sectors of the economy, with wages reported 

for over 34 million individuals in more than 

800 occupations. This article contributes to 

the growing body of literature analyzing the 

impact of firms’ compensation policies, and 

specifically, that which explores the topic of 

employer effects on wages.

The main contributions made by this 

research are the empirical estimates of the 

ways in which wages are influenced by the 

establishment at which the individual works. 

The decomposition of wages into employee 

and employer effects uses Ordinary Least 

Squares (OLS) regressions to partition the 

sum of squares of wages into worker and 

establishment components. The results show 

that employer effects contribute substanEstablishment wage differentials

Microdata from the BLS Occupational Employment Statistics 

program are providing researchers a fresh approach to use in 

studying how wages are influenced by the establishment in which 

an individual works

tially to earnings differences—the results 

from the basic model show that controlling for detailed occupation, establishment 

dummies account for more than one-fifth 

of individual wage variation. The results also 

show that these large employer effects can 

be only partially explained by observable 

characteristics, such as the location, size, age, 

and industry of the establishment.

In order to examine the breadth of the establishment wage differentials across occupations, correlations of occupational wages 

within establishments were calculated. The 

results are striking—establishments that 

pay well for one occupation also pay well for 

others. Even after controlling for observable 

establishment characteristics, positive wage 

correlations within establishments for occupations that are closely related were found, 

as well as for occupations that one would not 

expect to be closely related in the production 

process. This empirical finding may offer interesting implications for theories that attempt to explain the source of establishment 

wage differentials.

Background and literature review

Empirical estimates of establishment wage 

differentials. Establishment wage differentials are defined as the wage premium which 

controls for occupation and individual characteristics, and is common to all individuals 

in an establishment. While economists have 

known about these differentials ever since 

studies of employer wage policies were undertaken in the 1940s and 1950s, it is only 

Julia I. Lane is senior 

vice president and 

director of Economics, 

Labor, and Population 

Studies at the National 

Opinion Research 

Center (NORC), the 

University of Chicago. 

e-mail: Lane-Julia@

norc.uchicago.edu

Laurie A. Salmon is a 

supervisory economist 

and James R. Spletzer 

is a senior research 

economist, both in the 

Office of Employment 

and Unemployment 

Statistics, Bureau of 

Labor Statistics. E-mail: 

Salmon.Laurie@bls.

gov and Spletzer.Jim@

bls.gov
Establishment Wage Differentials

 Monthly Labor Review • April 2007

France account for 25 percent of wage variation conditional on observed worker characteristics and account for 

19 percent of wage variation conditional on both observed 

and unobserved worker heterogeneity.5 These results demonstrate that using longitudinal microdata to account for 

unobserved differences across individuals diminishes but 

does not remove the estimated employer effect on wages.

Theoretical explanations for establishment wage 

differentials. Erica Groshen’s classic 1991 reference 

effectively documented the theoretical explanations for 

establishment wage differentials.6 She proposed and 

evaluated five explanations as the reasons why individual 

wages vary among employers. These explanations for establishment wage differentials can also be found in the 

somewhat older and more firmly established industry 

wage differentials literature.7

The first explanation is that of labor quality, in which 

employers systematically sort workers by ability as predicted by team production models. Groshen offers two 

key reasons explaining why the sorting model is not the 

sole source of establishment wage differentials. First, differentials are estimated conditional on controls for occupation, and Groshen argues that detailed occupational 

information can serve as a proxy quite effectively for 

standard human capital variables. Similarly, industry wage 

differentials are estimated conditional on human capital 

controls, and these differentials still exist after controlling 

for unobserved individual ability in a longitudinal analysis. 

Second, it is difficult to reconcile the sorting explanation 

with the finding that establishment and industry wage 

differentials apply to all occupations.

A second explanation offered for the existence of establishment wage differentials is that of compensating differentials. Compensating differentials are defined as a wage 

premium paid to workers compensating them for undesirable working conditions. This explanation is problematic 

because the risk of injury is occupation specific, and does 

not necessarily apply to all workers in the establishment. 

Furthermore, the industry wage differentials literature 

has empirically examined and rejected the hypothesis of 

compensating differentials as an explanation for the wage 

differentials.

A third explanation suggested for the existence of establishment wage differentials is that costly information 

may generate random variation in wages across employers. 

For example, employers may profit from individuals who 

find it costly to search for alternative wage offers, or employers who hire infrequently may not have adjusted their 

pay structure since their last hiring cycle. Groshen rejects 

recently, with the advent of large electronically linked 

employer-employee micro-databases, that systematic statistical analyses of establishment wage differentials have 

been conducted. The empirical strategy used by almost all 

of these recent studies has been to define the differentials 

as the percentage of individual wage variation accounted 

for by adding establishment indicators to a regression that 

already includes controls for occupation and worker characteristics.

In 1991, Erica Groshen wrote the seminal article in 

the modern literature.1 Using data for six manufacturing 

industries from the Bureau of Labor Statistics Industry 

Wage Surveys, she decomposed earnings variation into 

occupational and establishment differentials as well as the 

interaction between the two. She found that establishments contribute substantially to earnings differences—when controlling for occupation, establishment 

wage differentials account for a sizeable amount of individual wage variation, ranging from a low of 12 percent in 

the cotton and man-made textiles industry to a high of 58 

percent in the industrial chemicals industry.

Groshen’s methodology and basic findings have been 

replicated with other data in recent studies. Using data 

from 241 establishments that responded to the Bureau of 

Labor Statistics White Collar Pay Survey, and controlling 

for individual worker characteristics, Stephen Bronars and 

Melissa Famulari found that 18 percent of individual wage 

variation is due to establishment wage differentials.2 Using 

data on 50,000 managerial positions in 39 companies, and 

controlling for job characteristics and job requirements, 

K. C. O’Shaughnessy, David Levine, and Peter Cappelli 

found that 8 to 9 percent of individual wage variation is 

due to firm (or establishment) wage differentials.3 Finally, 

in a study of the Brazilian and Chilean labor markets, 

Alejandra Mizala and Pilar Romaguera report that 7 to 

9 percent of Brazilian wage variation and 6 to 18 percent 

of Chilean wage variation can be attributed to firm wage 

differentials.4

These studies cited above use cross-sectional data with 

multiple individuals per establishment (or firm) and report 

estimates of differentials controlling for observed differences across individuals. It is natural to wonder whether 

these estimated differentials might be measuring unobserved differences in average worker skill across establishments, which would result from a sorting of individuals 

into establishments based on characteristics unobserved 

by the data analyst. Evaluating this hypothesis requires 

panel data with multiple observations per individual and 

multiple individuals per establishment. John Abowd and 

Francis Kramarz show that firm wage differentials in 
Monthly Labor Review • April 2007 

this explanation based on evidence that employer wage 

differentials are persistent over time.

A fourth explanation proposed for the existence of establishment wage differentials is efficiency wages. Efficiency wages refer to employers paying their workers more 

than the market-clearing wage in order to increase worker 

productivity. Efficiency wage theories, particularly those 

that emphasize the humanistic qualities of morale, loyalty, 

and teamwork, offer one explanation as to why workers in 

all occupations receive the establishment wage premium. 

Unfortunately, little, if any, direct empirical evidence has 

been found to exist that fully supports a relationship of 

this nature between efficiency wages and establishment 

wage differentials.

A fifth explanation is a model in which wage variation across employers results from workers bargaining over 

rents, or employers sharing profits with employees for other 

reasons. These models can generate the result that the 

establishment wage premium covers all occupations. The 

bargaining models are difficult to evaluate, however, especially their applicability outside the union sector. Groshen 

finds some support for rent-sharing models, citing research from the empirical literature which tends to show 

a positive relationship between an individual’s wage and 

the employer’s or the industry’s profits.

The literature on employer-size wage differentials also 

offers and evaluates similar explanations regarding the 

reasons why the individuals’ wages are associated with 

the establishment where they work.8 Briefly, the evidence 

from this literature suggests that theories based on compensating differentials, union avoidance, monitoring, and 

rent sharing accruing from product market power contribute little to explaining the employer-size wage differential. Sorting is a more likely possibility: Charles Brown 

and James Medoff find that labor-quality variables reduce 

the simple size coefficients by roughly one-half, and controlling for unobserved labor quality in a longitudinal 

fixed-effects regression reduces the size coefficients by 

an additional 5 to 45 percent.9 Even so, there remains a 

significant size effect after controlling for both observed 

and unobserved labor quality. Kenneth Troske uses linked 

employer-employee microdata that allows him to evaluate 

explanations which cannot be analyzed using most databases.10 He finds that more skilled workers tend to work 

together, as predicted by team production models, and 

this grouping reduces the employer-size wage premium 

by approximately 20 percent. However, Troske concludes 

that a large and significant employer-size wage premium 

still exists and remains unexplained.

A recent and comprehensive analysis of employer effects on wages is provided by John Abowd and Francis 

Kramarz.11 Their study decomposes estimates of a simply 

estimated employer differential into components that are 

due to unobserved individual heterogeneity and unobserved firm heterogeneity. Using data for both France and 

the United States, Abowd and Kramarz find that 45 to 

50 percent of the “raw” industry wage differential is due 

to unobserved firm heterogeneity, and 71 to 76 percent of 

the “raw” firm size wage differential is due to unobserved 

firm heterogeneity. While the sources of the unobserved 

firm heterogeneity remain unknown, these empirical estimates document that employer effects on wages do indeed 

exist.

The wage decomposition methodology

This article’s empirical analysis is based on the methodology used by Erica Groshen.12 It has a measure of log 

wages Wiej for individual “i” in establishment “e” in occupation “j.” By decomposing the variation in wages into 

components attributable to occupational differentials, establishment differentials, and differences across individuals, and following Groshen, the following four regressions 

are estimated:

(Occ)

Wiej = μ + OCCjα + εiej,

(Est)

Wiej = μ + ESTeβ + εiej,

(Main)

Wiej = μ + OCCjα + ESTeβ + εiej,

(Cell)

Wiej = μ + OCCjα + ESTeβ + (OCCj*ESTe)γ + εiej.

In these regressions, OCCj is a vector of dummy variables 

indicating the occupation, ESTe is a vector of dummy 

variables indicating the establishment, and (OCCj*ESTe) 

is a vector of dummy variables indicating an occupationalestablishment job cell.

This wage decomposition partitions the sum of squares 

of wages into its various components. As Groshen mentions, this statistical technique avoids imposing structure 

on unbalanced data. The OES microdata are unbalanced, 

with a different number of workers across occupations and 

a different number of occupations across establishments. 

The R-squareds from each of the four regressions are the 

key to the decomposition (not reported are the regression 

coefficients α, β, or γ). Notational definitions for these Rsquareds are R2

Occ, R2Est, R2Main, and R2Cell.
Establishment Wage Differentials

 Monthly Labor Review • April 2007

As seen from the first three regressions above, log wages 

are regressed on vectors of occupation and establishment 

indicators separately, and then on both sets of indicators 

together (the main-effects model). The marginal contribution of establishment indicators to the main-effects model, relative to the regression with occupation indicators 

only, measures the portion of wage variation associated 

unambiguously with the establishment indicators. This is 

calculated as (R2

Main – R2Occ). Similarly, the marginal contribution of occupation indicators is calculated as (R2

Main

– R2

Est) and measures the portion of wage variation associated unambiguously with the occupation indicators.

The explanatory power of occupation and establishment together in the main-effects model does not necessarily equal the sum of the marginal contributions to 

the main-effects model from the establishment indicators and from the occupation indicators. This difference, 

which is measured as (R2

Est + R2Occ – R2Main), is referred 

to as the “joint” explanatory power of occupation and establishment. This joint contribution is nonzero if there is 

any sorting of occupations across establishments. Positive 

sorting occurs if high-wage occupations are concentrated in high-wage establishments (R2

Est + R2Occ > R2Main), 

whereas negative sorting occurs if high-wage occupations 

are concentrated in low-wage establishments (R2

Est + R2Occ

< R2

Main). Research taken from the existing literature has 

shown positive sorting does occur between occupational 

wage differentials and establishment wage differentials.13

In the fourth regression above, the job-cell interactions measure the wage premium paid to a particular 

occupation in a particular establishment above or below 

the wage premium predicted by the occupational and the 

establishment differentials. The relative contribution of 

the job cells in our wage decomposition is measured as 

(R2

Cell – R2Main). The explanatory power of job cells captures what Erica Groshen and David Levine refer to as 

the “internal (wage) structure effect.”14 In a wage regression, the job cells can reflect many factors. For example, 

the initial phases of an establishment’s production process 

may resemble the average in the industry requiring workers of average ability, but its finishing process may require 

workers of higher-than-average ability. Another example 

may be that the wage profile in the establishment is tilted, 

either because of on-the-job training given to entry-level 

workers, or as a result of deferring wages in order to offer 

workers incentives not to shirk in their duties. The job-cell 

effects could also reflect differences in occupational tenure 

across establishments. 

The final contribution to wages is the individual contribution. This is measured as (1 – R2

Cell) and is the portion 

of the total sum of squares of wages that cannot be explained by occupation and establishment indicators. This 

individual contribution is undoubtedly due to unobserved 

wage effects that result from gender, education, tenure, or 

other individual attributes that are not captured by the 

interactions of the occupation and establishment indicators.

In summary, four regressions of log wages on various 

combinations of occupation and establishment dummy 

variables are estimated, with the focus on the R-squareds 

from these four regressions. Simple comparisons of these 

R-squareds provide information on occupational and 

establishment wage differentials, the degree of occupational sorting across establishments, the importance of 

employer-specific wage structures, and the importance of 

unobserved individual heterogeneity (controlling for occupation and establishment).

The data

As stated in the beginning of this article, microdata from 

the Occupational Employment Statistics (OES) program 

at the Bureau of Labor Statistics (BLS) were used. The OES

is an annual mail survey measuring occupational employment and wage rates by geographic area and by industry. 

Approximately 400,000 establishments are surveyed each 

year. The OES survey covers all full-time and part-time 

wage and salary workers in nonfarm industries. The survey 

does not cover the self-employed, owners and partners in 

unincorporated firms, household workers, or unpaid family workers. In 1996, the OES program began collecting 

wage-rate data along with occupational-employment data 

in every State. The survey is designed as a three-year sample, with one-third of both the certainty and noncertainty 

strata sampled each year.

The 1996 and 1997 microdata were used in this 

analysis. The sample had 573,586 establishments with no 

imputations of wage or employment data.15 It included 

occupation and wage information for all of the 34,453,430 

individuals employed in these establishments, along with 

information on the location, industry, size, and age of each 

establishment.

The OES survey asks establishments to fill out the elements of a matrix, in which occupations are listed on the 

rows and various wage ranges are listed in the columns. 

For each occupation, respondents are asked to report the 

number of employees paid within specific wage intervals. 

An example of the OES survey form, with many of the 

occupations omitted for presentation purposes, is given in 

the appendix. Separate OES survey forms are designed for 
Monthly Labor Review • April 2007

each industry group and list the occupations that are typical in the industry. Survey forms contain between 50 and 

225 OES occupations, depending on the industry classification and size class of the sampled establishments. If an 

occupation is not listed on a survey form, the respondent is 

asked to include the information on a supplemental page. 

To reduce paperwork and respondent burden, no survey 

form contains every OES occupation.

The occupational data in the 1996 and 1997 OES surveys 

are based on the 1980 Standard Occupational Classification (SOC) System. Occupations are classified based upon 

work performed, skills, education, training, and credentials. 

There are 824 detailed occupations in this OES microdata. 

In some of the analysis, these 824 detailed (5-digit) occupational codes were aggregated into 7 major (1-digit) occupations: Management, Professional, Sales, Clerical, Services, 

Agricultural, and Production.

The wage information provided by establishments in 

the OES survey is recorded in intervals for either hourly 

or annual rates of pay. (See appendix.) The actual values 

used for these intervals are the mean wage of all workers 

within the interval, as computed from the National Compensation Survey for that year.16 All of the wages used in 

this analysis were measured, in real terms, as the natural 

logarithm of hourly rates of pay.17

The obvious strengths of the OES microdata for economic 

analysis are the sample size and the level of occupational 

detail. Specifically, there are more than half a million 

establishments in our sample, with wages reported for over 

34 million individuals in more than 800 occupations. As 

such, the OES data can be viewed as a type of matched 

employer-employee microdata. The second strength of the 

OES is the employer-reported occupational data. Although 

the dataset contains no information regarding the worker’s 

demographic characteristics (such as age, race, or gender) 

or the worker’s labor-market information (such as tenure, 

experience, or training), it should be noted that the detailed 

occupational information should be a proxy for a worker’s 

skills. This latter point will be considered in the discussion 

of the empirical estimates.

Empirical wage decompositions

Basic results. The results of our wage decomposition are 

shown in table 1. In the first column, estimates using the 

seven 1-digit occupation measures are reported. In the 

second column, estimates using the 824 5-digit occupation measures are reported. The first four rows report the 

R-squareds from the regressions described earlier. These 

regressions are estimated from the sample of more than 

34 million individuals.18 The next five rows report the 

various contributions of occupation and establishment to 

wage variation.

The R-squareds in the fourth row of table 1 demonstrate that knowing an individual’s occupation and workplace provides substantial information towards explaining 

individual wage variation. More than 72 percent of wage 

variation is explained by knowing the individual’s 1-digit 

occupation and establishment, and close to 88 percent of 

wage variation is explained by knowing the individual’s 

5-digit occupation and establishment. This implies that 

approximately 12 percent of wage variation is left to unobserved individual heterogeneity (although it is acknowledged that this is probably an underestimate because of 

the use of interval data).

The importance of the information contained in the 

detailed occupational categories becomes clear from an 

analysis of the first row in table 1. In the first column, 

the seven 1-digit occupation indicators explain more than 

28 percent of wage variation. In the second column, the 

824 5-digit occupation indicators explain more than 54 

percent of wage variation. This empirically confirms the 

belief that the OES occupational data provide meaningful information about the work performed in the job, as 

well as the skills, education, training, and credentials of 

the persons performing the work. The R-squareds in the 

second row illustrate that establishment indicators alone 

explain approximately half of individual wage variation.

In the lower half of table 1, the decomposition of individual wage variation into its component parts is reported. 

 Table 1. Wage variance decomposition

Item (1) (2)

R2: Wiej = Occ dummies 0.2870 0.5466

R2: Wiej = Est dummies .4955 .4955

R2: Wiej = Occ + Est .6468 .7552

R2: Wiej = Occ * Est .7252 .8798

Occupation .1513 .2597

Joint occupation and establishment .1357 .2869

Establishment .3598 .2086

Job cell .0784 .1246

Individual .2748 .1202

One-digit occupation Yes —

Five-digit occupation — Yes

NOTE: 34,453,430 individuals. Wages are measured in natural logarithms: 

Mean=2.5133, Std.Dev.=0.5446.

There are 7 1-digit occupations, 824 5-digit occupations, and 573,586 

establishments.
Establishment Wage Differentials

 Monthly Labor Review • April 2007

By looking at the second column, which is based on regressions of log wages on detailed-occupation dummies 

and establishment dummies, it may be seen that 26 percent of wage variation is associated unambiguously with 

occupation, and 21 percent of wage variation is associated 

unambiguously with information on the individual’s establishment. An important part to understand is the sorting among occupations and establishments—this joint 

contribution accounts for 29 percent of wage variation. 

The final portion of the explained wage variation is the 

job-cell contribution, which accounts for slightly more 

than 12 percent of wage variation. The residual 12 percent 

of wage variation in the OES data is due to unobserved 

variation across individuals within a job cell.

It is worthwhile to compare the results of this study’s 

wage decomposition with the results reported by Erica 

Groshen.19 If a computation is run on the simple average 

across the six industries reported by Groshen, her results 

fall in between the results reported in columns 1 and 2 of 

table 1. For example, Groshen’s estimates imply that occupation indicators account for a mean of 20 percent of wage 

variation, and establishment indicators account for a mean 

of 32 percent of wage variation. This article’s estimates of 

the occupation effect range from 15 to 26 percent, and 

the estimates of the establishment effect range from 21 

to 36 percent. Estimates of the joint-sorting effect (14 to 

29 percent), the job-cell effect (8 to 12 percent), and the 

individual effect (12 to 27 percent) are also comparable 

to the means of the estimates reported by Groshen (17 

percent, 10 percent, and 22 percent, respectively).

The estimates in table 1 provide interesting insight into the labor market and 

the wage-setting practices of businesses. 

The occupation and establishment information in the OES data explain most of 

the wage variation across individuals. Not 

surprisingly, detailed information on the 

individual’s occupation explains a sizable 

amount of wage variation. Building on 

a small but growing literature, substantial establishment wage differentials are 

found.

Sensitivity analysis. The R-squared of 

0.8798 in table 1 is unusually high if it 

is compared with most earnings regressions based on worker surveys. This article 

is not the first study to find such a high 

R-squared when employers are included: 

Erica Groshen finds that “occupation and 

establishment identity alone can explain over 90 percent 

of wage variation among blue-collar workers.”20 It is 

notable that this high R-squared is achieved despite the 

fact that education and other individual determinants of 

wages are not available, confirming that occupation serves 

as a strong proxy for these factors. This is also supported 

by the finding that the residual individual component falls 

from 0.27 to 0.12 when moving from 1-digit to 5-digit 

occupation controls.

However, it is possible that, despite the fact that the OES

survey contains some of the most detailed and accurate 

occupational data available in any dataset, the R-squared 

may be inflated for technical reasons—the wage intervals 

in which the data are reported may be “too wide” relative 

to the wage variation within establishments. Clearly, as 

the occupational classifications become more detailed, or 

as the wage intervals become wider, the average number 

of wage intervals reported per job cell will decrease and 

the R-squareds will increase. In the longer working paper 

version of this article, we have examined the possibility 

that this may be a source of bias by undertaking an extensive sensitivity analysis. Specifically, in that version, an 

econometric framework was presented that simulates how 

the interval method of collecting individual wage data affects the estimates from our wage decomposition. It was 

found that collecting individual wage data as intervals 

in an establishment survey does not distort the conclusions drawn from our wage decomposition. Indeed, the 

sensitivity analysis in the longer working paper supports 

the notion that an important source of earnings variation 

 Table 2. The effect of observable establishment characteristics 

on empirical wage decompositions

Item (1) (2) (3) (4) (5) (6)

R2: Wiej = X 0.0833 0.0243 0.0727 0.1294 0.2955 0.3469

R2: Wiej = Occ + X .5884 .5499 .5684 .5658 .6104 .6515

Establishment effect .2086 .2086 .2086 .2086 .2086 .2086

Explained .0418 .0033 .0218 .0192 .0638 .1049

Unexplained .1668 .2053 .1868 .1894 .1448 .1037

County controls Yes — — — — Yes

Age controls — Yes — — — Yes

Size controls — — Yes — — Yes

Major industry controls — — — Yes — Yes

4-digit industry controls — — — — Yes Yes

NOTE: 34,453,430 individuals. Wages are measured in natural logarithms: Mean=2.5133, 

Std.Dev.=0.5446.

There are 7 1-digit occupations, 824 5-digit occupations, and 573,586 establishments. 

There are 3,194 counties, 5 age categories, 9 size categories, 10 major industries, and 

937 4-digit industries.
Monthly Labor Review • April 2007 

comes from between, rather than within, establishment 

variation.21

A closer examination of establishment wage differentials. In 

column 2 of table 1, 20.9 percent of wage variation is 

found to be attributable to differences across establishments. This provides strong evidence for establishment 

wage differentials. These estimated differentials, however, 

might simply reflect cost-of-living differences across establishments in different geographical areas, or might be 

acting as a proxy for other characteristics, such as size or 

industry. The importance of these effects is explored by 

modifying the decomposition to include establishmentlevel explanatory variables, such as age, size, industry, and 

county in the right-hand side of the wage regression.

The wage decomposition is now based on five regressions, for which the additional regression is:

(Occ,X)

Wiej = μ + OCCjα + Xeδ + εiej.

The components of Xe are dummy variables for industry, 

county, age, and size. The R-squared from this fifth regression is notationally defined as R2

Occ,X. Because these 

explanatory variables are linear combinations of the establishment dummies, the establishment contribution of the 

wage decomposition can be decomposed into two pieces: 

the explained and the unexplained contribution. The explained component of the establishment effect is defined 

as (R2

Occ,X – R2Occ), and the unexplained component of the 

establishment effect as (R2

Main – R2Occ,X). These two components sum to the total establishment effect in table 1, 

which is calculated as (R2

Main – R2Occ).

The wage decompositions controlling for the effects of 

observable establishment characteristics are presented in 

table 2. In column 1, the wage decomposition controlling 

for any county effects, including cost-of-living differences 

that are common within counties, are presented. These 

county controls account for one-fifth of the estimated establishment wage differentials (0.0418/0.2086), and thus 

local area differences explain some of the reasons why 

wages vary across establishments. Similarly, in columns 2 

through 5 of table 2, the conclusion is reached that age, 

size, and industry can each explain only a small portion 

of the reasons why wages vary across establishments. 

When all observable effects are controlled for together in 

column 6 of table 2, half of the estimated establishment 

wage differentials are accounted for. It may be concluded 

that establishment wage differentials can be only partially 

explained by observable establishment characteristics, and 

thus, establishment wage differentials are an important 

explanation for the reasons why wages vary across individuals.

Further empirical results. Many of the explanations put 

forward for the existence of employer effects on wages 

vary in importance for different industries. For example, 

capital-labor complementarity should be more important 

in the goods-producing industries than in the serviceproviding industries, unionization rates vary dramatically across industries, and skill sorting should be more 

important in industries that produce heterogeneous 

output. The results presented in table 3 show noticeable 

differences across major industries. Establishment wage 

differentials are most important in construction, mining, 

manufacturing, and transportation and public utilities 

(TCPU); they are least important in public administration; 

finance, insurance and real estate (FIRE); agriculture; and 

services. Establishment wage differentials explain 37 percent of wage variation in construction, yet only 16 percent 

of wage variation in the services industry. A number of 

reasons for these industry differences are possible: the traditional goods-producing industries are more unionized 

than the other sectors (with the exception of public administration), and these industries may well have greater 

variation in capital usage.

Interestingly, the construction and services industries 

are also quite different with regard to the contribution of 

occupational sorting: this component of the wage decomposition contributes little to variation in earnings in construction, but is quite important in services. This suggests 

that establishments in the construction industry bundle 

their workers in very similar ways, while establishments 

in the services industry bundle their workers very differently.

It is equally rewarding to analyze differences by establishment size. As seen in table 4, the importance of 

establishment wage differentials drops markedly and 

monotonically with the size of the establishment. Establishment wage differentials explain 30 percent of wage 

variation for establishments with two to nine employees, 

yet explain 16.5 percent of wage variation for the largest 

establishments. Also, it may be seen that the percentage 

of the establishment effect which can be explained by observed characteristics rises with the size of the establishment. The finding that small establishments exhibit more 

variation, both total and unexplained, in their contribution 

to wages is consistent with the notion that small establishments are more idiosyncratic than large establishments 

with regard to their personnel and paysetting practices.22
Establishment Wage Differentials

10 Monthly Labor Review • April 2007

 Table 3. Wage variance decomposition, by major industry

Item Agriculture Mining Construction Manufacturing TCPU

R2: Wiej = X 0.2819 0.4187 0.2511 0.3542 0.3114

R2: Wiej = Occ .5960 .4858 .3332 .5112 .4496

R2: Wiej = Occ + X .6596 .7042 .5325 .6765 .5826

R2: Wiej = Est .4340 .5284 .4556 .5144 .4844

R2: Wiej = Occ + Est .7666 .7829 .7017 .7855 .7171

R2: Wiej = Occ * Est .8921 .9114 .8595 .9110 .8565

Occupation .3326 .2545 .2461 .2711 .2327

Joint occupation and establishment .2634 .2313 .0871 .2401 .2169

Establishment .1706 .2971 .3685 .2743 .2675

Explained .0636 .2184 .1993 .1653 .1330

Unexplained .1070 .0787 .1692 .1090 .1345

Job cell .1255 .1285 .1578 .1255 .1394

Individual .1079 .0886 .1405 .0890 .1435

Number of individuals 268,958 180,110 1,358,346 6,020,917 1,895,225

Number of establishments 10,995 3,744 47,434 73,390 31,136

Number of 5-digit occupations 229 287 391 643 502

Item Wholesale Retail FIRE Services Public 

administration

R2: Wiej = X 0.1612 0.1912 0.2032 0.2937 0.2207

R2: Wiej = Occ .4778 .4575 .5319 .6075 .4282

R2: Wiej = Occ + X .5547 .5516 .6111 .6769 .5615

R2: Wiej = Est .3880 .3784 .3465 .4360 .2909

R2: Wiej = Occ + Est .7063 .6932 .7028 .7630 .6111

R2: Wiej = Occ * Est .8789 .8466 .8376 .8802 .7626

Occupation .3183 .3148 .3563 .3270 .3202

Joint occupation and establishment .1595 .1427 .1756 .2805 .1080

Establishment .2285 .2357 .1709 .1555 .1829

Explained .0769 .0941 .0792 .0694 .1333

Unexplained .1516 .1416 .0917 .0861 .0496

Job cell .1726 .1534 .1348 .1172 .1515

Individual .1211 .1534 .1624 .1198 .2374

Number of individuals 1,568,727 4,367,477 1,553,429 10,914,875 6,325,366

Number of establishments 53,433 134,886 36,408 167,371 14,789

Number of 5-digit occupations 559 534 409 759 669

NOTE: 34,453,430 individuals. Wages are measured in natural logarithms: 

Mean=2.5133, Std.Dev.=0.5446. There are 7 1-digit occupations, 824 5-digit 

occupations, and 573,586 establishments. 

There are 3,194 counties, 5 age categories, 9 size categories, 10 

major industries, and 937 4-digit industries. Explanatory variables “X” 

are county, age, size, and 4-digit industry.
Monthly Labor Review • April 2007 11

Occupational wages within establishments

The empirical evidence from the wage decompositions 

highlights the importance of the establishment itself for 

understanding the variation of individual wages. Even 

after controlling for observable characteristics that vary 

across establishments, substantial evidence of establishment wage differentials was found. By definition, these 

establishment wage differentials measure the wage premium paid to all workers in the establishment, regardless 

of occupation. This study now turns toward examining the 

correlations of occupational wages within establishments. 

The analysis here is motivated by the team-production 

model, well described by Michael Kremer.23 Simply put, 

in this model, workers of similar skill will be grouped 

together in firms—highly skilled supervisors will work 

with highly skilled production workers. This reflects the 

complex nature of a multi-stage production process that 

requires the coordinated and successful completion of distinct tasks. In many production processes, it is not possible 

for several low-skilled workers to substitute for one highskilled worker. Empirically, this should result in a positive 

correlation of occupational wages within establishments.

The analysis in this section is similar to previous work 

of William Dickens and Lawrence Katz, as well as previous work of Stephen Bronars and Melissa Famulari.24

The objective of the correlation analysis is to examine the 

breadth of the establishment wage differentials across occupations, with the goal being an enhanced understanding 

of their effects. For example, in a manufacturing plant, it is 

 Table 4. Wage variance decomposition, by establishment size

Item

Size

=1

Size

2–9

Size

10–15

Size

16–25

Size

26–50

Size

51–100

Size

101–250

Size

251–500

Size

>500

R2: Wiej = X 0.6535 0.2756 0.2844 0.3032 0.3191 0.3335 0.3373 0.3630 0.3042

R2: Wiej = Occ .4735 .4692 .5082 .5366 .5575 .5666 .5670 .5826 .5438

R2: Wiej = Occ + X .8125 .5595 .5946 .6213 .6361 .6505 .6586 .6888 .6590

R2: Wiej = Est 1.000 .5392 .4991 .4940 .4994 .5022 .4958 .4932 .3858

R2: Wiej = Occ +Est 1.000 .7684 .7589 .7626 .7646 .7655 .7632 .7714 .7088

R2: Wiej = Occ *Est 1.000 .9270 .9136 .9079 .9008 .8960 .8875 .8843 .8288

Occupation .0000 .2292 .2598 .2686 .2652 .2633 .2674 .2782 .3230

Joint occupation 

and establishment .4735 .2400 .2484 .2680 .2923 .3033 .2996 .3044 .2208

Establishment .5265 .2992 .2507 .2260 .2071 .1989 .1962 .1888 .1650

Explained .3390 .0903 .0864 .0847 .0786 .0839 .0916 .1062 .1152

Unexplained .1875 .2089 .1643 .1413 .1285 .1150 .1046 .0826 .0498

Job cell .0000 .1586 .1547 .1453 .1362 .1305 .1243 .1129 .1200

Individual .0000 .0730 .0864 .0921 .0992 .1040 .1125 .1157 .1712

Number of 

individuals 3,149 1,098,076 1,292,496 1,806,070 3,073,260 3,890,886 5,477,999 3,880,169 13,931,325

Number of 

establishments 3,149 177,200 106,272 90,111 86,388 55,087 36,111 11,280 7,988

Number of

5-digit occupations 377 791 802 806 815 819 821 812 816

NOTE: 34,453,430 individuals. Wages are measured in natural logarithms: 

Mean=2.5133, Std.Dev.=0.5446. There are 7 1-digit occupations, 824 5-digit 

occupations, and 573,586 establishments. 

There are 3,194 counties, 5 age categories, 9 size categories, 10 

major industries, and 937 4-digit industries. Explanatory variables “X” are 

county, age, size, and 4-digit industry.
Establishment Wage Differentials

12 Monthly Labor Review • April 2007

 Figure 1. Mean occupational wages, manufacturing industry

1.5

2

2.5

3

3.5

4

Production Supervisors and Machinists

(Correlation=0.6094)

Accountants and Machinists

(Correlation=0.4283)

1.5

2

2.5

3

3.5

4

Accountants and Production Supervisors

(Correlation=0.4118)

1.5

2

2.5

3

3.5

4

Janitors and Machinists

(Correlation=0.6116)

1.5

2

2.5

3

3.5

4

Janitors and Production Supervisors

(Correlation=0.5500)

1.5

2

2.5

3

3.5

4

Accountants and Janitors

(Correlation=0.4087)

1.5

2

2.5

3

3.5

4

NOTE: Wages are measured in natural logarithms. Sample is 338 establishments in the manufacturing industry with at least two 

employees in each of the following 5-digit occupations: machinists, production supervisors, accountants, and janitors.
Monthly Labor Review • April 2007 1

expected that the wages of machinists and production supervisors would be positively correlated, as they work side 

by side on the assembly line. It is less likely, however, that 

wages of the accountants or the janitors in this manufacturing plant would be positively correlated with the wages 

of the machinists and the production supervisors.

An examination of the data reveals that while the correlations across closely related occupations are quite high, 

supporting a team-production hypothesis, correlations 

are also surprisingly high across unrelated occupations. In 

figure 1, continuing with the example from the previous 

paragraph, the average wages of one occupation against 

the average wages of another occupation in the same establishment are graphed.25 Not surprisingly, it was found 

that the wages of machinists and the wages of production 

supervisors are closely correlated (the correlation is 0.61). 

Also found were that the wages of accountants are positively correlated with the wages of machinists and production supervisors (the correlations are 0.43 and 0.41), 

and the wages of janitors are positively correlated with 

the wages of machinists and production supervisors (the 

correlations are 0.61 and 0.55). Perhaps most surprisingly, 

the wages of accountants are highly correlated with the 

wages of janitors in the same establishment (the correlation is 0.41).

Consistent with the earlier analysis of establishment 

wage differentials outlined in this article, the enormous 

heterogeneity in wages across the manufacturing establishments that is evident in figure 1 deserves mention. For 

example, the establishment mean ln (wage) of accountants 

in this sample ranges from 2.1 to 3.9 (with a mean of 

2.94 and a standard deviation of 0.26). This heterogeneity 

is consistent with the findings of John Haltiwanger, Julia 

Lane, and James Spletzer, who outline a model wherein 

an unobserved business “type” generates heterogeneity in 

establishment productivity and wages.26 Furthermore, the 

findings in figure 1 of skill complementarity across occupations within the establishment fit quite nicely with 

Haltiwanger, Lane, and Spletzer’s model of complementarity between the “type” of business and the skill composition of its workforce.

The relationship of occupational mean wages within 

establishments is investigated more formally in table 5. 

For the seven major occupations, the correlation matrix 

of occupational mean wages within establishments is presented. Two correlations for each occupational pair are 

shown. The top correlation is unadjusted for observable 

establishment characteristics, whereas the bottom correlation is based on individual wage data with county, age, 

size, and major industry means removed.

 Table 5. Correlation of mean 1-digit occupational wages within establishments

Item Management Professional Sales Clerical Services Agricultural Production

Management 1

1

(N=378,960)

0.5054

.3964

(N=190,508)

0.5696

.3668

(N=177,866)

0.4503

.3346

(N=309,002)

0.3510

.2041

(N=123,393)

0.3668

.1798

(N=29,415)

0.3790

.1935

(N=234,127)

Professional — 1

1

(N=242,710)

.4515

.2249

(N=95,201)

.4788

.3604

(N=212,116)

.4237

.2900

(N=91,243)

.3625

.1293

(N=20,786)

.4671

.2315

(N=126,181)

Sales — — 1

1

(N=263,965)

.5004

.2072

(N=179,827)

.3822

.0912

(N=67,313)

.3869

.2273

(N=12,940)

.5020

.2469

(N=145,992)

Clerical — — — 1

1

(N=410,387)

.5138

.4387

(N=128,401)

.4904

.3054

(N=32,757)

.4878

.3033

(N=255,165)

Services — — — — 1

1

(N=173,193)

.5827

.3351

(N=17,470)

.4602

.2591

(N=88,471)

Agricultural — — — — — 1

1

(N=41,203)

.5780

.3447

(N=25,329)

Production — — — — — — 1

1

(N=316,958)

NOTE: 573,586 establishments. Wages are measured in natural 

logarithms. Upper correlation: no controls for establishment characteristics. Lower correlation: controls for county, age, size, and major industry.
Establishment Wage Differentials

14 Monthly Labor Review • April 20

Looking at the data unadjusted for establishment characteristics, the average of the 21 off-diagonal correlations 

is 0.4614. This is very similar to the estimate of Stephen 

Bronars and Melissa Famulari, who report a correlation 

of mean occupational wages between professionals and 

nonprofessionals of 0.499.27 All these correlations in table 

5 are positive and statistically greater than zero at conventional levels of significance. This says that establishments 

that pay well for one occupation also pay well for all other 

occupations. One particularly interesting pattern is that 

all correlations below 0.4 are in the upper-right corner 

of the table—it would seem that the least skill matching within establishments occurs between traditional 

white-collar occupations (managers, professionals, and 

sales) and blue-collar occupations (services, agricultural, 

and production). The correlations in table 5 are consistent 

with theories which predict that workers are sorted into 

establishments based on skill.

As was seen with the wage-decomposition analysis, it 

is possible that these correlations are biased upward by not 

controlling for observable characteristics of the establishment. After removing the effects of county, age, size, and 

industry, it is clear that the correlations fall. The average 

off-diagonal correlation fell dramatically from 0.4614 

to 0.2700. The correlations remain quite large, however, 

and all the correlations remain statistically greater than 

zero. This leads to the conclusion that the unadjusted occupational mean correlations within establishments do 

measure cost-of-living differences, industry effects, or size 

effects to a large extent, but also they are measuring establishment-specific pay practices that are otherwise unobservable to the data analyst.

Discussion

Using a simple regression-based wage decomposition 

effectively documents the magnitude of occupation and 

establishment wage differentials, the sorting of high-wage 

occupations into high-wage establishments, and the 

extent of employer-specific wage structures—the wage 

premium paid to particular occupations in particular establishments above or below the wage premium predicted 

by the occupational and the establishment differentials. 

The key finding in this article is that an establishment can 

and does exert a significant effect on the wages of the individuals who work therein. It may be seen that controlling 

for detailed occupation, 21 percent of wage variation can 

be explained merely by knowing the individual’s particular 

establishment. Accounting for observable characteristics 

of the employer reduces these establishment wage differentials by half. Taking the empirical analysis one step further, it was shown that the establishment’s wage premium 

is correlated across major occupation groups within the 

establishment. These empirical estimates complement and 

enhance previous work on the topic of employer effects 

on wages.

One of the dominant themes running through the literature of employer effects on wages is that establishments 

systematically sort workers by skill. The existing literature 

has found that this sorting explains much but not all of 

the observed employer effects on wages. The findings documented in this article are certainly consistent with such a 

conclusion. In the wage decomposition described earlier, 

merely knowing the worker’s establishment explains 50 

percent of the observed wage variation across individuals. 

Controlling for the seven 1-digit occupation indicators 

lowers this wage variation explained by establishments to 

36 percent, and controlling for 5-digit occupation indicators lowers this further to 21 percent. Because the detailed 

occupational information serves as a proxy for the worker’s 

skills, it was also found that controlling for skill explains 

much, but certainly not all, of the estimated establishment 

wage differentials in the raw data.

Another of the themes running through the literature 

is that establishment wage differentials are merely a proxy, 

at least in part, for unobserved characteristics of the establishment that are correlated with wages. The results found 

are consistent with this hypothesis. To the extent that 

differences across establishments in working conditions, 

costs of living, rent sharing, and capital-labor ratios can 

be proxied for by observable establishment characteristics, 

such as county, age, size, and industry, it was found that 

controlling for these characteristics lowers the estimated 

establishment wage differentials from 21 percent of wage 

variation to 10 percent.

The question remaining is how to explain the estimated establishment wage differentials. Any explanation 

proposed must simultaneously account for the finding 

that the establishment wage differentials are common to 

workers in all occupations in the establishment.

One possible explanation is that the observed differentials simply reflect differences in unobserved labor quality 

across establishments, and that more detailed information 

on individual ability and human capital would serve to 

eliminate the differentials. To the extent that this explanation is true, differentials support the sorting theory; to 

the extent that it is not, differentials support variations 

in establishment pay practices. Testing this hypothesis is 

beyond the capabilities of this dataset, for it does not have 

information on worker characteristics, such as education, 
Monthly Labor Review • April 2007 1

age, tenure, or training. In addition, there are several reasons to doubt that this hypothesis is the sole explanation 

of the estimated differentials. First, the work of Erica 

Groshen and David Levine suggests (but does not prove) 

that occupation adequately controls for standard measures of human capital.28 Moreover, in the work by K. C. 

O’Shaughnessy, David Levine, and Peter Cappelli, it was 

found that measures of skill and job characteristics do not 

explain much of the difference in wages across employers 

(although these measures of skill do explain quite a bit of 

wage variation across individuals).29 The findings of John 

Abowd and his colleagues, who have access to longitudinal linked employer-employee microdata and are thus 

able to control for unobserved skill using person-specific 

dummy variables, suggest that unmeasured heterogeneity 

across individuals explains some but not all of the estimated employer effects on wages.30 Finally, it is difficult to 

theorize how unobserved ability and human capital could 

be important contributors to wage differentials across all 

occupations in the establishment—such as janitors and 

accountants.

Another possibility is that the observed differentials reflect differences in technology or capital across establishments. Recent work using establishment microdata has 

illustrated the striking amount of heterogeneity across establishments within narrowly defined aggregates.31 While 

this study used establishment characteristics such as age, 

size, and industry to serve as a proxy for such differences, 

it would be useful to incorporate establishment-level information on inputs to (and outputs from) the production 

process into the analysis. However interesting and worthwhile this line of research would be, it may prove unlikely 

that capital intensity or technology per se would produce 

establishment wage differentials that are common to all 

occupations—again, the example of janitors and accountants comes to mind.

Any explanation for the existence of establishmen

wage differentials will, in all likelihood, rest on a combination of theories. Empirical work from recent analysis 

of matched employer-employee data shows that higherskilled workers not only work together in the same establishment, but also tend to work with higher-quality 

capital and technology.32 Modeling these basic humancapital results, augmented with a theory of why human 

resource pay policies might differ across establishments, 

should show how the gains from skill sorting and capitallabor complementarities can be extended to workers in all 

occupations in the establishment. Thoughts such as these 

run throughout the existing body of literature that examines the reasons why the wages of individuals are, to an 

extent not entirely understood, affected at a variety of levels by their employer. Additional theoretical and empirical 

research will have much more information to offer. 

Notes

1 Erica L. Groshen, “Sources of Intra-Industry Wage Dispersion: 

How Much Do Employers Matter?” The Quarterly Journal of Economics,

August 1991, pp. 869–884.

2 Stephen G. Bronars and Melissa Famulari, “Wage, Tenure, and 

Wage Growth Variation Within and Across Establishments,” Journal 

of Labor Economics, April 1997, pp. 285–317.

3 K. C. O’Shaughnessy, David I. Levine, and Peter Cappelli, 

“Changes in Managerial Pay Structures, 1986–1992, and Rising 

Returns to Skill,” National Bureau of Economic Research (NBER) 

Working Paper No. 7730 (Cambridge, MA, NBER, 2000). 

4 Alejandra Mizala and Pilar Romaguera, “Wage Differentials 

and Occupational Wage Premia: Firm-Level Evidence for Brazil and 

Chile,” Review of Income and Wealth, June 1998, pp. 239–257.

5 John M. Abowd and Francis Kramarz, “Inter-Industry and Firmsize Wage Differentials in France and the United States,” unpublished 

paper (Ithaca, NY, Cornell University, 1999.) 

6 Erica L. Groshen, “Five Reasons Why Wages Vary Among Employers,” Industrial Relations, Fall 1991, pp. 350–381.

7 Key references that have influenced the industry wage differentials 

literature are William T. Dickens and Lawrence F. Katz, “Inter-Industry Wage Differences and Theories of Wage Determination,” NBER 

Working Paper No. 2271 (Cambridge, MA, NBER, 1987); Lawrence 

F. Katz and Lawrence H. Summers, “Industry Rents: Evidence and 

Implications,” Brookings Papers on Economic Activity (Washington, DC, 

The Brookings Institution, 1989), pp. 209–275; and Alan B. Krueger and 

Lawrence H. Summers, “Efficiency Wages and the Inter-Industry Wage 

Structure,” Econometrica, March 1988, pp. 259–294.

8 One survey of the employer-size wage differentials literature is 

Walter Y. Oi and Todd L. Idson, “Firm Size and Wages,” in Handbook 

of Labor Economics, edited by Orley Ashenfelter and David Card (Amsterdam, North-Holland Press, 1999), pp. 2165–2214.

9 Charles Brown and James Medoff, “The Employer Size-Wage Effect,” Journal of Political Economy, October 1989, pp. 1027–1059.

10 Kenneth R. Troske, “Evidence on the Employer Size-Wage Premium from Worker-Establishment Matched Data,” The Review of 

Economics and Statistics, February 1999, pp. 15–26.

11 Abowd and Kramarz, “Inter-Industry and Firm-size Wage Differentials,” unpublished paper (Cornell University, 1999.)

12 Groshen, “Sources of Intra-Industry Wage Dispersion” Quarterly 

Journal of Economics, August 1991, pp. 869–884.

13 See Groshen, “Sources of Intra-Industry Wage Dispersion” Quarterly Journal of Economics, August 1991, pp. 869–884; and Erica L. 
Establishment Wage Differentials

16 Monthly Labor Review • April 20

Groshen and David I. Levine, “The Rise and Decline (?) of U.S. Internal 

Labor Markets,” Working Paper No. 9819, Federal Reserve Bank of 

New York (New York, 1998). 

14 Groshen and Levine, “The Rise and Decline (?) of U.S. Internal 

Labor Markets,” Working Paper No. 9819, Federal Reserve Bank of 

New York (1998).

15 The response rate for the OES survey is 78 percent (thus we 

have survey responses from roughly 624,000 of the 800,000 sampled 

establishments). The remaining sample reduction is to exclude the establishments that report employment or wage data for some but not 

all occupations.

16 The interval mean for the bottom interval may vary for States 

with a minimum wage above the Federal minimum. The interval mean 

for the top interval is set in nominal terms at $60.01. This upper wage 

interval contains 0.7 percent of the individuals in our sample (244,727 

/ 34,453,430). It has been found that the results from the wage decomposition are not sensitive to the point estimate used for this upper 

interval: the establishment effects reported in table 1 are 20.86 percent 

using the point estimate of $60.01, and would be 20.78 percent using 

a point estimate of $70.01 and 20.69 percent using a point estimate 

of $80.01.

17 Given that the wage data can be reported as either annual or 

hourly, there is a concern that the establishment wage differentials 

could reflect hours differences across establishments. The example of 

banking comes to mind: a bank with “bankers’ hours” may have tellers 

working six hours per day, whereas a full-service bank may have tellers working eight hours per day. Our estimated establishment wage 

differentials could be affected if earnings for occupations with hours 

variation across establishments are reported on an annual basis. This 

potential bias should be mitigated, however, by the fact that the OES 

survey respondents are instructed to classify part-time workers according to an hourly rate.

18 The R-squareds from a regression using 34 million individuals 

are identical to the R-squareds from a regression using 7,778,248 

“cells” weighted by employment, where a “cell” is a wage interval within 

an establishment-occupation job cell.

19 Groshen, “Sources of Intra-Industry Wage Dispersion,” Quarterly Journal of Economics, August 1991, pp. 869–884. The authors 

recognize that it may be conceptually difficult to compare this study’s 

results (which are computed from a national sample) with Groshen’s 

results (which are computed from six industries). One purpose of this 

simple comparison is to demonstrate that the results from this study’s 

estimation, and in particular the high R-squareds, are similar to results 

from other data which use the same methodology.

20 This quote is from Groshen, “Sources of Intra-Industry Wage 

Dispersion,” Quarterly Journal of Economics, August 1991, p. 869.

21 The longer working paper version of this article is Julia I. Lane, 

Laurie A. Salmon, and James R. Spletzer, “Establishment Wage Differentials,” BLS Working Paper No. 403 (Washington, DC, U.S. Department of Labor, Bureau of Labor Statistics, March 2007). 

22 This conclusion mirrors the findings of John C. Haltiwanger, Julia 

I. Lane, and James R. Spletzer, “Wages, Productivity, and the Dynamic 

Interaction of Businesses and Workers,” Labour Economics, June 2007, 

pp. 575–602, which show that new businesses exhibit greater earnings 

heterogeneity than do mature businesses. 

23 Michael Kremer, “The O-Ring Theory of Economic Development,” The Quarterly Journal of Economics, August 1993, pp. 551–575.

24 Dickens and Katz, “Inter-Industry Wage Differences and Theories of Wage Determination,” NBER Working Paper No. 2271 (1987); 

and Bronars and Famulari, “Wage, Tenure, and Wage Growth Variation,” Journal of Labor Economics, April 1997, pp. 285–317. 

25 There are 47,633 manufacturing establishments with at least 1 

worker in any of the four occupations. We have selected the 338 manufacturing establishments with at least 2 workers in each of the four 

occupations.

26 Haltiwanger, Lane, and Spletzer, “Wages, Productivity, and Dynamic Interaction,” Labour Economics, June 2007, pp. 575–602.

27 Bronars and Famulari, “Wage, Tenure, and Wage Growth Variation,” Journal of Labor Economics, April 1997, pp. 285–317.

28 Groshen, “Sources of Intra-Industry Wage Dispersion,” Quarterly 

Journal of Economics, August 1991, pp. 869–884; and David I. Levine, 

“Can Wage Increases Pay for Themselves? Tests with a Production 

Function,” Economic Journal, September 1992, pp. 1102–1115.

29 O’Shaughnessy, Levine, and Cappelli, “Changes in Managerial Pay Structures, 1986–1992, and Rising Returns to Skill,” NBER 

Working Paper No. 7730 (2000).

30 See Abowd and Kramarz, “Inter-Industry and Firm-size Wage 

Differentials,” unpublished paper (Cornell University, 1999); John M. 

Abowd, Francis Kramarz, and David Margolis, “High Wage Workers 

and High Wage Firms,” Econometrica, March 1999, pp. 251–334; and 

John M. Abowd, Hampton Finer, and Francis Kramarz, “Individual 

and Firm Heterogeneity in Compensation: An Analysis of Matched 

Longitudinal Employer-Employee Data for the State of Washington,” 

in The Creation and Analysis of Employer-Employee Matched Data, edited 

by John C. Haltiwanger, Julia I. Lane, James R. Spletzer, Jules J. M. 

Theeuwes, and Kenneth R. Troske (Amsterdam, North-Holland Press, 

1999), pp. 3–24.

31 See, for example, Haltiwanger, Lane, and Spletzer, “Wages, Productivity, and Dynamic Interaction,” Labour Economics, June 2007, pp. 

575–602.

32 See Mark Doms, Timothy Dunne, and Kenneth R. Troske, 

“Workers, Wages, and Technology,” The Quarterly Journal of Economics, February 1997, pp. 253–290; and Haltiwanger, Lane, and Spletzer, 

“Wages, Productivity, and Dynamic Interaction,” Labour Economics, 

June 2007, pp. 575–602.
Monthly Labor Review • April 2007 17

A B C D E F G H I J K L

Hourly

(part-time or 

full-time)

under

$7.50

$7.50–

9.49

$9.50–

11.99

$12.00–

15.24

$15.25–

19.24

$19.25–

24.49

$24.50–

30.99

$31.00–

39.24

$39.25–

49.74

$49.75–

63.24

$63.25–

79.99

$80.00

and over

Annual

(full-time

only)

under

$15,600

$15,600–

19,759

$19,760–

24,959

$24,960–

31,719

$31,720–

40,039

$40,040–

50,959

$50,960–

64,479

$64,480–

81,639

$81,640–

103,479

$103,480–

131,559

$131,560–

166,399

$166,400

and over

A B C D E F G H I J K L

17-1011

A B C D E F G H I J K L

17-1012

A B C D E F G H I J K L

17-1021

A B C D E F G H I J K L

17-1022

A B C D E F G H I J K L

17-2011

A B C D E F G H I J K L

17-2021

A B C D E F G H I J K L

17-2031

A B C D E F G H I J K L

17-2041

Agricultural Engineers -

Biomedical Engineers -

Plan and design structures, such as private residences, office 

buildings, theaters, factories, and other structural property.

Apply knowledge of engineering technology and biological 

science to agricultural problems concerned with power and 

machinery, electrification, structures, soil and water 

conservation, and processing of agricultural products.

Surveyors -

Aerospace Engineers -

Collect, analyze, and interpret geographic information provided 

by geodetic surveys, aerial photographs, and satellite data. 

Research, study, and prepare maps and other spatial data in 

digital or graphic form. May work with Geographic Information 

Systems (GIS).

Cartographers and Photogrammetrists -

Plan and design land areas for such projects as parks and 

other recreational facilities, airports, highways, hospitals, 

schools, land subdivisions, and commercial, industrial, and 

residential sites.

Landscape Architects -

T

T

T

T

T

Make exact measurements and determine property boundaries

Provide data relevant to the shape, contour, gravitation, 

location, elevation, or dimension of land or land features on or 

near the earth's surface.

Perform a variety of engineering work in designing, 

constructing, and testing aircraft, missiles, and spacecraft.

Total

Employment

T

Architects, Except Landscape and Naval -

OCCUPATIONAL TITLE AND

DESCRIPTION OF DUTIES

NUMBER OF EMPLOYEES IN SELECTED WAGE RANGES

(Report Part-time Workers According to an Hourly Rate)

T

T

Chemical Engineers - T

Design chemical plant equipment and devise processes for 

manufacturing chemicals and products by applying principles 

and technology of chemistry, physics, and engineering.

Apply knowledge of engineering, biology, and biomechanical 

principles to the design, development, and evaluation of 

biological and health systems and products, such as artificial 

organs and medical information systems.

Appendix: Example of OES Survey Form