BLS WORKING PAPERS 

 

U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Employment and Unemployment Statistics 

Establishment Wage Differentials 

Julia I. Lane, The National Opinion Research Center (NORC) 

Laurie A. Salmon, U.S. Bureau of Labor Statistics 

James R. Spletzer, U.S. Bureau of Labor Statistics 

Working Paper 403

March 2007

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
Establishment Wage Differentials 

March 2007 

Abstract 

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 it is important to 

understand how employee and employer characteristics are related to wages, little is known 

about the magnitude and relation of these wage effects. This is primarily due to the lack of 

microdata which links individuals to the establishments where they work, but also due to 

technical difficulties associated with separating out employee and employer effects. This 

paper uses data from the Occupational Employment Statistics program at the Bureau of Labor 

Statistics that permit both of these issues to be addressed. Our results show that employer 

effects contribute substantially to earnings differences across individuals. We also find that 

establishments that pay well for one occupation also pay well for others. This paper 

contributes to the growing literature that analyzes firmsí compensation policies, and 

specifically the topic of employer effects on wages. 

Julia I. Lane 

The National Opinion Research Center (NORC) and IZA 

Laurie A. Salmon 

U.S. Bureau of Labor Statistics 

James R. Spletzer 

U.S. Bureau of Labor Statistics 

We are very grateful to many of our colleagues and to participants at various conferences and seminars for their 

useful comments on previous drafts of this paper. The views in this paper are those of the authors and do not 

necessarily reflect the views of the U.S. Bureau of Labor Statistics. 
1

I. Introduction 

 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 it is 

important to understand how employee and employer characteristics are related to wages, 

little is known about the magnitude and relation of these wage effects. This is primarily due 

to the lack of microdata which links individuals to the establishments where they work, but 

also due to technical difficulties associated with separating out employee and employer 

effects. In this paper, we use microdata from the Occupational Employment Statistics 

program at the Bureau of Labor Statistics that permit both of these issues to be addressed. 

These data 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 paper contributes to the growing literature that analyzes the impact of 

firmsí compensation policies, and specifically the topic of employer effects on wages. 

 The main contribution of this paper is the empirical estimates of how wages are 

influenced by the establishment at which the individual works. Our decomposition of wages 

into employee and employer effects is based on similar work by Groshen (1991b), which uses 

OLS regressions to partition the sum of squares of wages into worker and establishment 

components. Our results show that employer effects contribute substantially to earnings 

differences -- the results from our basic model show that controlling for detailed occupation, 

establishment dummies account for over one-fifth of wage variation. We modify Groshenís 

decomposition and 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 differential across 

occupations, we calculate the correlations of occupational wages within establishments. Our 

results are striking -- we find that establishments that pay well for one occupation also pay 

well for others. Even after controlling for observable establishment characteristics, we find 

positive wage correlations within establishments for occupations that are closely related, as 

well as for occupations that one would not expect to be closely related in the production 

process. This empirical finding has interesting implications for theories that attempt to 

explain the source of establishment wage differentials. 
2

II. Background and Literature Review 

IIa) Empirical Estimates of Establishment Wage Differentials 

 Establishment wage differentials (EWDs) are defined as the wage premium, 

controlling for occupation and individual characteristics, that is common to all individuals in 

an establishment. While economists have known about EWDs since the studies of employer 

wage policies in the 1940s and 1950s -- see the literature review in Segal (1986) -- it is only 

recently with the advent of large 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 EWDs 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. 

 Groshen (1991b) is the seminal article in this modern literature. Using data for six 

manufacturing industries from the Bureau of Labor Statisticsí Industry Wage Surveys, 

Groshen 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 12 

percent in the cotton and man-made textiles industry to 58 percent in the industrial chemicals 

industry. 

 Groshenís methodology and basic findings have been replicated with other data in 

recent studies. Using data from 241 establishments responding to the Bureau of Labor 

Statisticsí White Collar Pay Survey, and controlling for individual worker characteristics, 

Bronars and Famulari (1997) find that 18 percent of individual wage variation is due to 

establishment wage differentials. Extending this work to provide a comparison of the United 

States and Denmark, Bronars, Bingley, Famulari, and Westergard-Nielsen (1999) report that 

20 percent of variation in Danish while collar pay and 36 percent of variation in Danish blue 

collar pay is attributable to establishment wage differentials. Using data on 50,000 

managerial positions in 39 companies, and controlling for job characteristics and job 

requirements (as measured by Hay points), O'Shaughnessy, Levine, and Cappelli (2000) find 
3

that 8 to 9 percent of individual wage variation is due to firm wage differentials. Finally, a 

study of the Brazilian and Chilean labor markets, Mizala and Romaguera (1998) 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. 

 The studies just cited use cross-sectional data with multiple individuals per 

establishment (or firm), and report estimates of EWDs controlling for observed heterogeneity 

across individuals. It is natural to wonder whether these estimated EWDs might be measuring 

differences in average worker skill across establishments, which would result from a sorting 

of individuals into establishments based on characteristics unobserved by the econometrician. 

Evaluating this hypothesis requires panel data with multiple observations per individual and 

multiple individuals per establishment. Abowd, Kramarz, and their coauthors show that firm 

wage differentials in 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.1 These results demonstrates that using 

longitudinal microdata to account for unobserved heterogeneity diminishes but does not 

remove the estimated employer effect on wages. 

IIb) Theoretical Explanations for Establishment Wage Differentials 

 Groshen (1991a) is the classic reference regarding theoretical explanations for 

establishment wage differentials. She proposes and evaluates five explanations for why 

individual wages vary among employers. These explanations for establishment wage 

differentials can also be found in the somewhat older and more well established industry wage 

differentials literature.2

 

1

 The statistics presented in this paragraph are from personal communications with John Abowd. He has 

graciously provided us with the R-squareds from exact solutions, instead of the R-squareds that are based upon 

approximations and are reported in Abowd, Kramarz, and Margolis (1999). For the record, there is nothing 

wrong with the approximations. Differences between the approximations and the exact solutions lies in the fact 

that insufficient computing capacity for analysis of the French data did not allow for the inclusion of enough 

terms in the approximation to get the approximate solution close to the full least squares solution. The paper by 

Abowd, Finer, and Kramarz (1999) did all the calculations using the same approximations with data from the 

State of Washington, without computer constraints, and the R-squareds based on the approximations are fine. 

See Abowd and Kramarz (1999) for further details. 

2

 Key references that have influenced the industry wage differentials literature are Dickens and Katz (1987), 

Katz and Summers (1989), Krueger and Summers (1988), and Murphy and Topel (1987). 
4

 The first explanation is that of labor quality, where employers systematically sort workers 

by ability as predicted by team production models. Groshen offers two key reasons for why 

the sorting model is not the sole source of establishment wage differentials. First, EWDs are 

estimated conditional on controls for occupation, and Groshen argues that detailed 

occupational information proxies quite well 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 for the existence of establishment wage differentials is that of 

compensating differentials. Compensating differentials are defined as a wage premium paid 

to workers to compensate them for undesirable working conditions. This explanation is 

problematic since the risk of injury is occupation specific, rather than applying 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 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 (1991a) rejects this explanation based on evidence that employer wage 

differentials are persistent. 

 The fourth explanation proposed by Groshen (1991a) for the existence of 

establishment wage differentials is efficiency wages. Efficiency wage theories, particularly 

those that emphasize morale, loyalty, and teamwork, can explain why workers in all 

occupations receive the establishment wage premium. With efficiency wages, differences 

across establishments resulting from a variety of factors such as monitoring costs, turnover 

costs, or managerial tastes generates the heterogeneity necessary to observe establishment 

specific pay policies. Unfortunately, there is little, if any, direct empirical evidence on the 

relationship between efficiency wages and establishment wage differentials. 
5

 A fifth explanation is a model where 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. However, the bargaining models are difficult to evaluate, especially their 

applicability outside the union sector. Groshen finds some support for rent sharing models, 

citing the empirical literature which tends to show a positive relationship between an 

individual's wage and the employer's or the industry's profits.3

 The literature on employer-size wage differentials also evaluates similar explanations 

regarding why the wages of individuals are associated with the establishment where they 

work (see Oi and Idson, 1999, for a recent survey). 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 employersize wage differential. Sorting is a more likely possibility: Brown and Medoff (1989) 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 a further five to forty-five percent. Even so, there remains a significant 

size effect after controlling for both observed and unobserved labor quality. AlbÊk, Arai, 

Asplund, Barth, and Madsen (1998) also find that the sorting of workers on unobserved 

characteristics does not explain the estimated size effect. Troske (1999) uses linked 

employer-employee microdata that allows him to evaluate explanations that can not be 

analyzed using most databases. He finds that more skilled workers tend to work together, as 

predicted by team production models, and this matching 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 

Abowd and Kramarz (1999). Building on previous work in Abowd, Kramarz, and Margolis 

(1999) and Abowd, Finer, and Kramarz (1999), this study decomposes estimates of a simply 

estimated employer differential into components that are due to unobserved individual 

 

3

 Hildreth and Oswald (1997) is a recent reference documenting the rent sharing hypothesis. However, Margolis 

and Salvanes (2000) present evidence that suggests that a sizable portion of the positive correlation between firm 

profits and worker earnings is due to interactions between the unobservable characteristics of the firmís 

workforce and the bargaining power of workers at different stages of the business cycle. 
6

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 ìrawî industry wage differential 

is due to unobserved firm heterogeneity, and 71 to 76 percent of the ìrawî 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 exist. This type of analysis based on linked employer-employee microdata is 

bringing the topic of employer effects on wages back to the forefront of the theoretical and the 

empirical literature. 

III. The Wage Decomposition Methodology 

 Our empirical analysis is based on the methodology used by Groshen (1991b). We 

have a measure of log wages Wiej for individual "i" in establishment "e" in occupation "j." 

We want to decompose the variation in wages into components attributable to occupational 

differentials, establishment differentials, and differences across individuals. Following 

Groshen, we estimate the following four regressions: 

 (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 occupational-establishment job cell. 

 This wage decomposition partitions the sum of squares of wages into its various 

components. As Groshen (1991b) 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 (we will 
7

not report the regression coefficients α, β, or γ). We notationally define these R-squareds as 

R2

Occ, R2Est, R2Main, and R2Cell. 

 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 just occupation indicators, 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 - R2Est), 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 non-zero if there is any sorting of 

occupations across establishments. Positive sorting occurs if high wage occupations are 

concentrated in establishments with high wage premiums (R2

Est + R2Occ > R2Main), whereas 

negative sorting occurs if high wage occupations are concentrated in establishments with 

lower wage premiums (R2

Est + R2Occ < R2Main). The existing literature -- Groshen (1991b) and 

Groshen and Levine (1998) -- has found positive sorting between occupational wage 

differentials and establishment wage differentials. 

 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 capture what Groshen and Levine (1998) refer to as the 

"internal (wage) structure effect." 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, but the 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 
8

in order to offer workers incentives not to shirk. 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 can not be explained by 

occupation and establishment indicators. This individual contribution is undoubtedly due to 

unobserved wage effects from gender, education, tenure, or other individual attributes that are 

not captured by the interactions of the occupation and establishment indicators. 

 In summary, we estimate four regressions of log wages on various combinations of 

occupation and establishment dummy variables, and we 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). 

IV. The Data 

 We use microdata from the Occupational Employment Statistics (OES) program at the 

Bureau of Labor Statistics (BLS). The OES is an annual mail survey measuring occupational 

employment and wages 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 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 non-certainty strata sampled each year. 

 We use the 1996 and 1997 microdata in our analysis. Our sample has 573,586 

establishments with no imputations of wage or employment data.4

 These establishments 

 

4

 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. For example, if the human resources clerk 

filling out the survey does not have wage data for upper management, the clerk may report all employment data 

but not all wage data. 
9

cover 34,453,430 workers categorized by occupation and wage interval. We also have 

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

 The OES survey asks establishments to fill out the elements of a matrix, where 

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 Figure 1. Separate OES survey forms are 

designed for 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 survey 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 our OES microdata. In some of our analysis, we aggregate these 824 detailed 

(five-digit) occupational codes into seven major (one-digit) occupations: Management, 

Professional, Sales, Clerical, Services, Agricultural, and Production. 

 As seen in Figure 1, the wage information provided by establishments in the OES 

survey is recorded in intervals for either hourly or annual rates of pay. The actual values we 

use for these intervals are the mean wage of all workers within the interval as computed from 

the National Compensation Survey for that year.5

 In the following section and in the 

appendix of this paper, we discuss the econometrics and the empirical consequences of wage 

data reported as interval means. All of the wages used in our analysis are measured, in real 

terms, as the natural logarithm of hourly rates of pay.6

 

5

 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% of the individuals in our sample (244,727 / 34,453,430). We have found that the results from our 

wage decomposition are not sensitive to the point estimate used for this upper interval: the establishment effects 

we report in Table 1 are 20.86% using the point estimate of $60.01, and would be 20.78% using a point estimate 

of $70.01 and 20.69% using a point estimate of $80.01. 

6

 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 ìbankersí hoursî may have tellers working six hours per day, whereas a full service bank may 

have tellers working eight hours per day. Our estimated EWDs could be affected if earnings for occupations 
10

 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, covering over 34 million individuals in more than 800 

occupations. As such, the OES data can be viewed as a proxy for matched employeremployee microdata. The second strength of the OES is the employer-reported occupational 

data. Although the dataset contains no information regarding the workerís demographic 

characteristics (such as age, race, or gender) or other labor market characteristics (such as 

tenure, experience, or training), we note that the detailed occupational information should 

proxy for a workerís skills. We will return to this latter point in the discussion of our 

empirical estimates. 

V. Empirical Wage Decompositions 

Va) Basic Results 

 We present the results of our wage decomposition in Table 1. In the first column, we 

report estimates using the seven one-digit occupation measures. In the second column, we 

report estimates using the 824 five-digit occupation measures. The first four rows report the 

R-squareds from the regressions described earlier.7 The bottom 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 individualís 

occupation and workplace go a very long way to explaining individual wage variation. More 

than 72 percent of wage variation is explained by knowing the individual's one-digit 

occupation and establishment, and almost 88 percent of wage variation is explained by 

knowing the individual's five-digit occupation and establishment. This implies that 

approximately 12 percent of wage variation is left to unobserved individual heterogeneity 

(although we acknowledge that this is probably an underestimate because of our use of 

interval data). 

 

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. 

7

 These regressions are estimated from our microdata covering 34 million individuals. The R-squareds from a 

regression using 34 million individuals are identical to the R-squareds from a regression using 7,778,248 ìcellsî 

weighted by employment, where a ìcellî is a wage interval within an establishment-occupation job cell. 
11

 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 onedigit occupation indicators explain more than 28 percent of wage variation. In the second 

column, the 824 five-digit occupation indicators explain more than 54 percent of wage 

variation. This empirically confirms our 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 

about half of the wage variation. This regression is of interest other than its intermediary role 

in our wage decomposition. Kremer and Maskin (1996) have developed an index which 

captures the degree to which workers with similar wages are grouped across establishments. 

The Kremer and Maskin segregation index is nothing more than the R-squared from a 

regression of individual wages on a vector of establishment dummies. Our estimate of .4955 

is roughly comparable to other estimates from the United States.8

 In the bottom half of Table 1, we report the decomposition of wage variation into its 

component parts. Looking at the second column, we find that 26 percent of wage variation is 

associated unambiguously with occupation, and 21 percent of wage variation is associated 

unambiguously with information on the worker's establishment. An important part of the 

story is the sorting between occupations and establishments -- we find that this joint 

contribution accounts for 29 percent of wage variation. And the final portion of the explained 

wage variation is the job cell contribution, which accounts for just over 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. 

 

8

 Davis and Haltiwanger (1991) report that 51 to 58 percent of the total variance in wages is accounted for by 

the dispersion in mean wages across plants. One can manipulate Groshen's (1991b) estimates in her Table 2 and 

conclude that the R-squareds from regressions of log wages on establishment dummies range from .17 to .75, 

with a simple mean across the six industries of .49. Bronars and Famulari (1997) report an R-squared of .45. 

The results in Lane, Lerman, and Stevens (1998) suggest that the proportion of wage variance explained by 

between firm variation is roughly .45. Outside the United States, Kramarz, Lolliver, and PelÈ (1996) report a 

wage-based segregation measure for France of .38 in 1986 and .48 in 1992, and Bronars, Bingley, Famulari, and 

Westergard-Nielsen (1999) report an R-squared of .35 for white collar workers and .46 for blue collar workers in 

Denmark. 
12

 It is interesting to compare the results of our wage decomposition with the results 

reported by Groshen (1991b).9 If we compute the simple average across the six industries 

reported by Groshen, her results fall in between the results we report 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. Our estimates of the occupation effect range from 15 to 26 

percent, and our estimates of the establishment effect range from 21 to 36 percent. Our 

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. We find, not surprisingly, that 

detailed information on the individual's occupation explains a sizable amount of wage 

variation. Building on a small but growing literature, we also find substantial establishment 

wage differentials. 

Vb) Sensitivity Analysis 

 The R-squared of .8798 in Table 1 is unusually high if one were to compare it to most 

earnings regressions based on worker surveys. We are not the first to find such a high Rsquared when employers are included: Groshen (1991b, page 869) finds that ìoccupation and 

establishment identity alone can explain over 90 percent of wage variation among blue-collar 

workers.î It is interesting to note that this high R-squared is achieved despite that fact that 

education and other individual determinants of wages are not available, confirming that 

occupation is a strong proxy for these factors. This is supported by our finding that the 

residual individual component falls from .27 to .12 when we move from one-digit to five-digit 

occupation controls. 

 

9

 We recognize that it may be conceptually difficult to compare our results (which are computed from a national 

sample) with Groshenís results (which are computed from six industries). One purpose of this simple 

comparison is to demonstrate that the results from our estimation, and in particular the high R-squareds, are 

similar to results from other data which use the same methodology. 
13

 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 ìtoo 

wideî 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 this section, we examine the possibility that this may be a source of bias by undertaking an 

extensive sensitivity analysis. 

 In our full sample, 62 percent of job cells contain all employment within one OES 

wage interval, whereas 16 percent of job cells contain employment in 3 or more OES wage 

intervals.10 The average number of OES wage intervals for a job cell is 1.66. Obviously 

these statistics will vary by establishment size. For the largest establishments (those with 

more than 500 employees), 37 percent of job cells contain employment in only one OES wage 

interval, whereas 38 percent of job cells contain employment in 3 or more OES wage 

intervals. We find it interesting that for these largest establishments, the R-squareds in the job 

cell regressions are still quite high at .8288 (see Appendix Table 1). 

 The technical appendix to this paper describes an econometric framework for 

analyzing how collecting wage data in intervals affects the R-squareds from our wage 

decomposition. The essence of this framework is to simulate a distribution of individual 

wages which can then be collapsed to intervals corresponding to the OES wage intervals. The 

following is a summary of our results. If a continuous distribution of individual wages were 

used as the dependent variable in the regression of wages on occupation dummies, we 

calculate that the R-squared would be .5292 instead of the .5466 we report in Table 1. 

Similarly, the R-squared for the regression on establishment dummies should be .4798 instead 

of the .4955 reported in Table 1. And our simulation suggests that after accounting for the 

effect of interval means, the R-squared for the main effect regression would fall from .7552 to 

.7312, and the R-squared for the job cell regression would fall from .8798 to .8518. 

Transforming these simulated R-squareds into the occupational and establishment 

contributions to wage variation, our estimates of {.2597, .2869, .2086, .1246, .1202} reported 

 

10 See Appendix Table 1 for these statistics. The unit of analysis in Appendix Table 1 is the job cell, which is a 

detailed 5-digit occupation within an establishment. 
14

in Table 1 would change to {.2514, .2778, .2020, .1206, .1482}. Each of the first four terms 

(the occupational effect, the joint effect, the establishment effect, and the job cell effect) falls 

slightly, and the residual individual effect rises from .1202 to .1482. We conclude that 

collecting individual wage data as intervals in an establishment survey does not distort the 

conclusions we draw from our wage decomposition.11 Indeed, this evidence supports the 

notion that an important source of earnings variation comes from between, rather than within, 

establishment variation. 

Vc) A Closer Examination of Establishment Wage Differentials 

 In column 2 of Table 1, we found that 20.9 percent of wage variation is attributable to 

differences across establishments. This is strong evidence for establishment wage 

differentials. However, these estimated EWDs 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. We explore the importance of these effects by 

modifying Groshenís (1991b) decomposition to include establishment level explanatory 

variables such as age, size, industry, and county in the right hand side of the wage regression. 

 Our wage decomposition is now based on five regressions, where the additional 

regression is: 

 (Occ,X) Wiej = μ + OCCjα + Xeδ + εiej, 

The components of Xe are dummy variables for industry, county, age, and size.12 The Rsquared from this fifth regression is notationally defined as R2

Occ,X. Since these explanatory 

variables are linear combinations of the establishment dummies, we can decompose the 

establishment contribution of the wage decomposition into two pieces: the explained and the 

 

11 We present some additional evidence in support of this conclusion. We took a cross-section of 

Unemployment Insurance wage records (described in Burgess, Lane, and Stevens, 2000) and regressed a 

continuous measure of annual earnings on firm dummies, and then ran a similar regression where the dependent 

variable has been recoded into point estimates corresponding to the OES hourly wage intervals. The R-squareds 

are quite similar: .55 with the continuous measure, and .57 with the interval measure. 

12 We considered allowing age and size to be continuous variables rather than a vector of dummy variables, but 

we adopted our dummy variable approach for several reasons. Intuitively, we maintain the spirit of "occupation 

first" followed by "establishment conditional on occupation" that is implicit in Groshen's (1991b) wage 

decomposition. Furthermore, the joint contribution is unaffected, which says that the sorting effect between 

occupations and establishments has not changed. And finally, we note that Abowd, Kramarz, and Margolis 

(1999) recoded their continuous size variable into dummy variables. 
15

unexplained contribution. We define the explained component of the establishment effect as 

(R2

Occ,X - R2Occ), and the unexplained component of the establishment effect as (R2Main - 

R2

Occ,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, we present the wage decomposition 

controlling for any county effects including cost of living differences that are common within 

counties. These county controls account for one-fifth of the estimated establishment wage 

differentials (.0418/.2086), and thus local area differences explain some of why wages vary 

across establishments. Similarly, in columns 2 through 5 of Table 2, we conclude that age, 

size, and industry can each only explain a small portion of why wages vary across 

establishments.13 When we control for all observable effects together in column 6 of Table 2, 

we account for half of the estimated establishment wage differentials. We conclude that 

establishment wage differentials can be only partially explained by observable establishment 

characteristics, and thus establishment wage differentials are an important explanation for 

why wages vary across individuals. 

Vd) 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 services providing 

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 3a show noticeable differences across major industries. Establishment wage 

differentials are most important in construction, mining, manufacturing, and TCPU, and are 

least important in public administration, FIRE, agriculture, and services. EWDs explain 37 

percent of wage variation in construction, yet only 16 percent of wage variation in the 

 

13 Since the largest size category of 500+ accounts for about 40 percent of the workers (but only 1.4 percent of 

the establishments), it is a worthwhile exercise to experiment with alternative size categories. We find that the 

amount of the establishment effect that can be explained using observed establishment characteristics is not 

sensitive to the categorization of the size variable. When we break out the 500+ category into deciles and use 18 

size class dummies (instead of the 9 used in Table 2), the size variable accounts for 11.7 percent of our estimated 

establishment effect (compared to 10.5 percent calculated from the statistics reported in Table 2). 
16

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 

3b, the importance of establishment wage differentials drops markedly and monotonically 

with the size of the establishment. EWDs explain 30 percent of wage variation for 

establishments with 2-9 employees, yet explain 16.5 percent of wage variation for the largest 

establishments. We also see that the percentage of the establishment effect that can be 

explained by observed characteristics rises with the size of the establishment. Our finding 

that small establishments have 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.14

VI. Occupational Wages Within Establishments 

 The empirical evidence from our wage decompositions highlights the importance of 

the establishment for understanding the variation of individual wages. Even after controlling 

for observable characteristics that vary across establishments, we find substantial evidence of 

establishment wage differentials. By definition, these establishment wage differentials 

measure the wage premium paid to all workers in the establishment, regardless of occupation. 

We now turn to examining the correlations of occupational wages within establishments. Our 

 

14 This conclusion mirrors the findings of Haltiwanger, Lane, and Spletzer (2006), who show that new 

businesses exhibit greater earnings heterogeneity than do mature businesses. 
17

analysis here is motivated by the team production model, well described by Kremer (1993). 

Simply put, in this model, workers of similar skill will match together in firms -- highly 

skilled supervisors will work with highly skilled production workers. This reflects the 

complex nature of a multi-stage production process, which 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 high skilled worker. Empirically, this should 

result in a positive correlation of occupational wages within establishments. 

 Our analysis in this section is similar to previous work by Dickens and Katz (1987), 

Bronars and Famulari (1997), and Bronars, Bingley, Famulari, and Westergard-Nielsen 

(1999). The goal of our correlation analysis is to examine the breadth of the establishment 

wage differentials across occupations. For example, in a manufacturing plant, we would 

expect the wages of machinists and production supervisors to be positively correlated since 

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 2, continuing with the example 

from the previous paragraph, we graph the average wages of one occupation against the 

average wages of another occupation in the same establishment.15 We find, not surprisingly, 

that the wages of machinists and the wages of production supervisors are closely correlated 

(the correlation is .61).16 We also find that the wages of accountants are positively correlated 

with the wages of machinists and production supervisors (the correlations are .43 and .41), 

and the wages of janitors are positively correlated with the wages of machinists and 

production supervisors (the correlations are .61 and .55). Perhaps most surprisingly, the 

wages of accountants are highly correlated with the wages of janitors in the same 

establishment (the correlation is .41). 

 

15 There are 47,633 manufacturing establishments with at least one 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. 

16 The correlation coefficient is the square root of the R-squared from an OLS regression of one occupational 

mean wage against another occupational mean wage. For example, the R-squared from a regression of the mean 

wages of machinists against the mean wages of production supervisors is .37 (=.61*.61). 
18

 Consistent with our earlier analysis of establishment wage differentials, the enormous 

wage heterogeneity across the manufacturing establishments that is evident in Figure 2 

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 Haltiwanger, Lane, and Spletzer (2006), who 

outline a model where some unobserved business "type" generates heterogeneity in 

establishment productivity and wages. Furthermore, our findings in Figure 2 of skill 

complementarity across occupations within the establishment fits quite nicely with 

Haltiwanger, Lane, and Spletzer's model of complementarity between the "type" of business 

and the skill composition of its workforce. 

 We investigate the relationship of occupational mean wages within establishments 

more formally in Table 4. For the seven major occupations, we present the correlation matrix 

of occupational mean wages within establishments. We present two correlations for each 

occupational pair. 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. 

 Looking at the data unadjusted for establishment characteristics, the average of the 21 

off-diagonal correlations is .4614. This is very similar to the estimate of Bronars and 

Famulari (1997), who report a correlation of mean occupational wages between professional 

and nonprofessionals of .499. All these correlations in Table 4 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 .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 4 are consistent with theories which 

predict that workers are sorted into establishments based on skill. 

 Just as with the wage decomposition analysis, it is possible that these correlations are 

biased upward by not controlling for observable characteristics of the establishment. After 

taking out the effects of county, age, size, and industry, it is clear that the correlations fall. 

The average off-diagonal correlation has fallen dramatically from .4614 to .2700. However, 
19

the correlations remain quite large, and all the correlations remain statistically greater than 

zero. This leads us to conclude that the unadjusted occupational mean correlations within 

establishments do measure cost of living differences, industry effects, or size effects to a large 

extent, but are also measuring establishment specific pay practices that are unobservable to 

the econometrician. 

VII. Conclusions and Discussion 

 Using the wage decomposition proposed by Groshen (1991b), we have documented 

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. Our key finding in this paper is the large effect that the establishment has on the 

wages of the individuals who work there. We find that controlling for detailed occupation, 21 

percent of wage variation can be explained by merely knowing the individual's establishment. 

Accounting for observable characteristics of the employer reduces these establishment wage 

differentials by half. Taking our empirical analysis one step further, we have 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 empirical work 

finds that this sorting explains much but not all of the observed employer effects on wages. 

Our findings are certainly consistent with this conclusion. In our wage decomposition, merely 

knowing the worker's establishment explains 50 percent of the observed wage variation across 

individuals. Controlling for the seven one-digit occupation indicators lowers this wage 

variation explained by establishments to 36 percent, and controlling for five-digit occupation 

indicators lowers this further to 21 percent. Since our detailed occupational information 

proxies for the workerís skills, we also find that controlling for skill explains much, but 

certainly not all, of the estimated establishment wage differentials in the raw data. 
20

 Another of the themes running through the literature is that establishment wage 

differentials merely proxy, at least in part, for unobserved characteristics of the establishment 

that are correlated with wages. Our results are consistent with this hypothesis. To the extent 

that differences across establishments in working conditions, cost of living, rent sharing, and 

capital-labor ratios can be proxied for by observable establishment characteristics such as 

county, age, size, and industry, we find that controlling for these characteristics lowers the 

estimated establishment wage differentials from 21 percent of wage variation to 10 percent. 

 We are now left with the question of how to explain our estimated establishment wage 

differentials. Any explanation we propose must simultaneously account for our 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 EWDs. To the extent that 

this explanation is true, EWDs support the sorting theory, to the extent that it is not, EWDs 

support variations in establishment pay practices. Testing this hypothesis is beyond the 

capabilities of our dataset, since we donít have information on worker characteristics such as 

education, age, tenure, or training. However, there are several reasons to doubt that this 

hypothesis is the sole explanation of the estimated EWDs. First, the work of Groshen (1991b) 

and Levine (1992) suggests (but does not prove) that occupation adequately controls for 

standard measures of human capital. In addition, work by O'Shaughnessy, Levine, and 

Cappelli (2000) finds that measures of skill and job characteristics do not explain much of the 

difference in wages across employers (although these measures of skill explain quite a bit of 

wage variation across individuals). The findings of John Abowd and his colleagues, who 

have access to longitudinal linked employer-employee microdata and are thus able to proxy 

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. And 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. 
21

 Another possibility is that the observed differentials reflect differences in technology 

or capital across establishments. Pekkarinen (2002) presents an interesting model and 

analysis along these lines. Recent work using establishment microdata has illustrated the 

striking amount of heterogeneity across establishments within narrowly defined aggregates -- 

see, for example, Haltiwanger, Lane, and Spletzer (2006). While we have used establishment 

characteristics such as age, size, and industry to proxy for such differences, it would be useful 

to incorporate establishment level information on inputs to (and outputs from) the production 

process into our analysis. However interesting and worthwhile this line of research would be, 

we believe it is 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. 

 We believe that any explanation for the existence of establishment wage differentials 

will rest on a combination of theories. Empirical work from recent analysis of matched 

employer-employee data shows that higher skilled workers not only work together in the same 

establishment, but also tend to work with higher quality capital and technology -- see Doms, 

Dunne, and Troske (1997) and Haltiwanger, Lane, and Spletzer (2006). Modeling these basic 

human capital results, augmented with a theory of why human resource pay policies might 

differ across establishments, should show how the gains from skill sorting and capital-labor 

complementarities can be spread to workers in all occupations in the establishment. These 

thoughts are not original to us, but run through the existing literature examining why the 

wages of individuals are affected by their employer. There is much more to be learned from 

additional theoretical and empirical research. 
22

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23

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25

Technical Appendix: OES Wages and Interval Econometrics 

The OES survey collects employee wage data in intervals. As is evident from Figure 1, this 

eliminates any wage heterogeneity across individuals within an interval. However, because 

there are multiple wage intervals for a given occupation on the OES survey form, there is still 

wage heterogeneity across individuals within job cells (where job cells are defined as an 

occupation within an establishment). In our decomposition, where we partition the total sum 

of squares into its components, this interval method of collecting individual wage data should 

reduce the residual variance attributable to individuals and thus increase the explained 

variance due to establishments and occupations. How severe might this problem be? In this 

appendix, we present an econometric framework for simulating how this data collection 

methodology affects the estimates from our wage decomposition. 

Assume that an individual's true ln(wage) is Yiej, but the data analyst observes Wiej -- the 

natural logarithm of the OES interval mean. The relationship between the observed wage and 

the true wage is Wiej=Yiej+ωiej, where ωiej measures how the individual's wage differs from the 

interval mean. For example, in Figure 1, wage interval "H" includes all employees earning 

between $19.25 and $24.24 per hour, and the OES interval mean for survey year 1997 is 

Wiej=21.43. With appropriate transformations to logarithms, ωiej in this example is bounded 

between -.1073 and .1232. We shall refer to ωiej as the "interval error." 

For any vector of explanatory variables X, the R-squared that we estimate from the regression 

Wiej=XiejβW+εiej is: 

















− −

− − = − ( )'( )) à )'( à ( 1 2

W W W W

W X W X R

W W

W

β β . 

But the "true" unobserved individual wage (Yiej) should have been used as the dependent 

variable in the regression, rather than the observed interval mean (Wiej). The R-squared that 

would have been estimated from the regression Yiej=XiejβY+εiej is: 

















− −

− − = − ( )'( )

) à )'( à ( 1 2

Y Y Y Y

Y X Y X R

Y Y

Y

β β

 

















− − − − − −

− − − − = − ({ } { })'({ } { })

) à )'( à ( 1

ω ω ω ω

ω β ω β

W W W W

W X W X Y Y

. 

If we assume that X'ω=0, so that Y W β

à βà = ,

17 then 

 

17 If X is a matrix of establishment indicators, a sufficient condition for X'ω=0 is that the mean of the interval 

error is zero for every establishment. While one can come up with examples of certain establishments where this 

sufficient condition may not be true, we have no reason to believe that X'ω≠0 in the overall sample. 
26

















− − − − − + − −

− − − + = − ( )'( ) 2( )'( ) ( )'( )

) 2 ' ' à )'( à ( 1 2

ω ω ω ω ω ω

β β ω ω ω

W W W W W W

W X W X W R

W W

Y . 

Because the point estimates for the interval wages are computed as the mean of the underlying 

wage distribution for each interval using data from the Employment Cost Index, we know that 

π=0 for each interval.18 Since W is a fixed value within each interval, it is straightforward to 

show that W'ω=0, and thus 

















− − +

− − + = − ω ωβ β ω ω

( )'( ) '

) ' à )'( à ( 1 2

W W W W

W X W X R

W W

Y . 

Given our assumptions, one can show that 2 2 RY < RW . Therefore, when using interval means 

rather than the true unobserved wages, the R-squareds that we obtain from our regressions 

overstate the contribution of occupation and establishment indicators to wage variation, and 

thus understate the residual contribution of unobserved individual heterogeneity. 

We now describe our simulation exercise that is based on this econometric framework. We 

have simulated a ln(wage) for 34,453,430 individuals from a normal distribution with mean 

2.5133 and standard deviation 0.5446 (this mean and standard deviation are reported in the 

footnotes to Table 1). We then compute the corresponding wage level, define the interval 

wage corresponding to the OES intervals reported in Figure 1, and also define the interval 

error ωiej as the difference between the individual's true wage and the natural logarithm of the 

interval wage. After forcing the interval error ωiej to be mean zero within intervals, we 

calculate all the terms necessary to compare 2 RY and 2 RW as defined in the formulas above. 

We report in Table 1 that the R-squared for the regression of wages on occupation dummies is 

.5466. If the individual wages instead of interval means were used as the dependent variable 

in the regression, we calculate that this R-squared should be .5292.19 Similarly, the R-squared 

for the regression on establishment dummies should be .4798 instead of the .4955 reported in 

Table 1. And our simulation suggests that after accounting for the effect of interval means, 

the R-squared for the main effect regression would fall from .7552 to .7312, and the Rsquared for the job cell regression would fall from .8798 to .8518. 

Transforming these simulated R-squareds into the occupational and establishment 

contributions to wage variation, our estimates of {.2597, .2869, .2086, .1246, .1202} reported 

in Table 1 would change to {.2514, .2778, .2020, .1206, .1482}. Each of the first four terms 

(the occupational effect, the joint effect, the establishment effect, and the job cell effect) falls 

 

18 This statement is not true for the uppermost interval, where the OES interval mean is set in nominal terms at 

$60.01. In our econometric framework, we censor the simulated wage distribution at this value and thus by 

definition impose a zero mean for this interval. 

19 The original R-squared of .5466 is calculated as [1-(4,633,689/10,219,022)]. The simulated R-squared of 

.5234 is calculated as [1-(4,633,689+335,893)/(10,219,022+335,893)], where ω'ω=335,893. 
27

slightly, and the residual individual effect rises from .1202 to .1482. We conclude that having 

individual wage data calculated as interval means does not distort the conclusions we draw 

from our wage decomposition. 
Figure 1: Example of OES Survey Form

Nonmetallic Minerals and Metal Mining Industries 

 Number of Employees in Selected Wage Ranges  A B C D E F G H I J K  Hourly Under 

$6.75 

$6.75-

8.49 

$8.50-

9.99 

$10.00-

11.24 

$11.25-

13.24 

$13.25-

15.74 

$15.75-

19.24 

$19.25-

24.24 

$24.25-

43.24 

$43.25-

$60.00 

$60.01 

and over 

Number of 

Employees 

Occupation Code and Title 

Annual Under 

$14,040 

$14,040-

17,659 

$17,660-

20,779 

$20,780-

23,999 

$23,400-

27,559 

$27,560-

32,759 

$32,760-

40,039 

$40,040-

50,439 

$50,440-

89,959 

$89,960-

124,820 

$124,821 

and over TOTAL 

13002 Financial Managers 15021 Mining, Quarrying, 

and Oil & Gas Well Drilling Managers 

19005 General Managers 

and Top Executives 

21114 Accountants and 

Auditors 

22105 Metallurgists and 

Metallurgical, Ceramic, and Materials Engineers 

22108 Mining Engineers, 

Including Mine Safety 

22311 Surveyors and 

Mapping Scientists 

22514 Drafters 24111 Geologists, 

Geophysicists, and Oceanographers 

Ö 98300 Helpers, 

Construction Trades and Extractive Workers 

98700 Freight, Stock, and 

Material Movers, Hand 
Table 1: Variance Decomposition 

 (1) (2) R2: Wiej = Occ Dummies .2870 .5466 R2: Wiej = Est Dummies .4955 .4955 R2: Wiej = Occ + Est .6468 .7552 R2: Wiej = Occ*Est .7252 .8798 Occupation .1513 .2597 Joint Occup & Estab .1357 .2869 Establishment .3598 .2086 Job Cell .0784 .1246 Individual .2748 .1202 One-Digit Occupation Yes Five-Digit Occupation Yes Source: OES unweighted microdata. 34,453,430 individuals. Wages are measured in natural logarithms: Mean=2.5133, Std.Dev.=0.5446. There are 7 One-Digit Occupations, 824 Five-Digit Occupations, and 573,586 establishments. 
Table 2: The Effect of Observable Establishment Characteristics on EWDs 

 (1) (2) (3) (4) (5) (6) R2: Wiej = X .0833 .0243 .0727 .1294 .2955 .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 See notes to Table 1. There are 3,194 counties, 5 age categories, 9 size categories, 10 major industries, and 937 4-digit industries. 
Table 3a: Variance Decomposition, by Major Industry 

Agricult Mining Constr Manuf TCPU 

Whole 

sale Retail FIRE Services

Public 

Admin 

R2: Wiej = X .2819 .4187 .2511 .3542 .3114 .1612 .1912 .2032 .2937 .2207 R2: Wiej = Occ .5960 .4858 .3332 .5112 .4496 .4778 .4575 .5319 .6075 .4282 R2: Wiej = Occ + X .6596 .7042 .5325 .6765 .5826 .5547 .5516 .6111 .6769 .5615 R2: Wiej = Est .4340 .5284 .4556 .5144 .4844 .3880 .3784 .3465 .4360 .2909 R2: Wiej = Occ + Est .7666 .7829 .7017 .7855 .7171 .7063 .6932 .7028 .7630 .6111 R2: Wiej = Occ*Est .8921 .9114 .8595 .9110 .8565 .8789 .8466 .8376 .8802 .7626 Occupation .3326 .2545 .2461 .2711 .2327 .3183 .3148 .3563 .3270 .3202 Joint Occup & Estab .2634 .2313 .0871 .2401 .2169 .1595 .1427 .1756 .2805 .1080 Establishment .1706 .2971 .3685 .2743 .2675 .2285 .2357 .1709 .1555 .1829  Explained .0636 .2184 .1993 .1653 .1330 .0769 .0941 .0792 .0694 .1333  Unexplained .1070 .0787 .1692 .1090 .1345 .1516 .1416 .0917 .0861 .0496 Job Cell .1255 .1285 .1578 .1255 .1394 .1726 .1534 .1348 .1172 .1515 Individual .1079 .0886 .1405 .0890 .1435 .1211 .1534 .1624 .1198 .2374 # Individuals 268,958 180,110 1,358,346 6,020,917 1,895,225 1,568,727 4,367,477 1,553,429 10,914,875 6,325,366 # Establishments 10,995 3,744 47,434 73,390 31,136 53,433 134,886 36,408 167,371 14,789 # 5-Digit Occupations 229 287 391 643 502 559 534 409 759 669 See notes to Tables 1 and 2. Explanatory variables "X" are county, age, size, and 4-digit industry. 
Table 3b: Variance Decomposition, by Establishment Size 

 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 .6535 .2756 .2844 .3032 .3191 .3335 .3373 .3630 .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 Occup & Estab .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 # 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 # Establishments 3,149 177,200 106,272 90,111 86,388 55,087 36,111 11,280 7,988 # 5-Digit Occupations 377 791 802 806 815 819 821 812 816 See notes to Tables 1 and 2. Explanatory variables "X" are county, age, size, and 4-digit industry. 
Figure 2: Mean Occupational Wages, Manufacturing Industry 

Production Supervisors and Machinists

(Correlation=.6094)

1.5

2

2.5

3

3.5

4

Accountants and Machinists

(Correlation=.4283)

1.5

2

2.5

3

3.5

4

Accountants and Production Supervisors

(Correlation=.4118)

1.5

2

2.5

3

3.5

4

Janitors and Machinists

(Correlation=.6116)

1.5

2

2.5

3

3.5

4

Janitors and Production Supervisors

(Correlation=.5500)

1.5

2

2.5

3

3.5

4

Accountants and Janitors

(Correlation=.4087)

1.5

2

2.5

3

3.5

4

Source: OES unweighted microdata. Wages are measured in natural logarithms. The axes in each chart are the average log wages of an occupation. 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. 
Table 4: Correlation of Mean One-Digit Occupational Wages Within Establishments 

 Management Professional Sales Clerical Services Agricultural Production Management 1 

1 

(N=378,960) 

.5054 .3964 

(N=190,508) 

.5696 .3668 

(N=177,866) 

.4503 .3346 

(N=309,002) 

.3510 .2041 

(N=123,393) 

.3668 .1798 

(N=29,415) 

.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) 

Source: OES unweighted microdata. 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. 
Appendix Table 1: Number of OES Wage Intervals per Job Cell (using 5-digit occupational data) 

 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 

1 Wage Interval 62.0% 100% 78.5% 72.5% 68.5% 63.7% 60.5% 56.1% 50.2% 37.2% 2 Wage Intervals 21.9% 16.6% 19.4% 20.9% 22.5% 23.0% 23.8% 24.9% 24.5% 3 Wage Intervals 9.0% 3.8% 5.6% 6.8% 8.3% 9.5% 11.0% 13.0% 16.5% 4 Wage Intervals 4.1% 0.9% 1.8% 2.5% 3.4% 4.1% 5.2% 6.7% 10.3% 5 Wage Intervals 1.8% 0.2% 0.5% 0.9% 1.3% 1.8% 2.4% 3.1% 5.9% 6-11 Wage Intervals 1.3% 0.0% 0.2% 0.4% 0.7% 1.1% 1.5% 2.1% 5.6% # Establishments 573,586 3,149 177,200 106,272 90,111 86,388 55,087 36,111 11,280 7,988 # Occupations 824 377 791 802 806 815 819 821 812 816 # Est-Occ Job Cells 4,672,533 3,149 570,569 513,548 584,286 769,135 737,501 724,029 333,670 436,646 # Est-Occ-Wage Intervals 7,778,248 3,149 728,316 713,824 862,849 1,219,805 1,234,635 1,298,082 651,500 1,066,088 Average # Wage Intervals  per Est-Occ Job Cell 

1.66 1.00 1.28 1.39 1.48 1.59 1.67 1.79 1.95 2.44 

R2: Wiej = Occ*Est .8798 1.000 .9270 .9136 .9079 .9008 .8960 .8875 .8843 .8288