BLS WORKING PAPERS

U.S. Department of Labor

U.S. Bureau of Labor Statistics

Office of Employment Research and Program Development

Outsourcing, Occupationally Homogeneous Employers, and Growing Wage Inequality in 

the United States

Elizabeth Weber Handwerker, U.S. Bureau of Labor Statistics

Working Paper 522

May 2020

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.

JEL codes: J31, J53, M51, L24, M55

Keywords: outsourcing, employer homogeneity, wage inequality, contracting out, employment polarization 
2

Outsourcing, Occupationally Homogeneous Employers, and Growing Wage Inequality in 

the United States

Elizabeth Weber Handwerker

U.S. Bureau of Labor Statistics

May, 2020

Abstract: This paper develops measures of the occupational homogeneity of employers as 

indicators of outsourcing. Findings are threefold. First, workers in low-wage occupations saw 

their employing establishments become more occupationally homogeneous during 2002-2016.

Second, wages are strongly related to occupational homogeneity, particularly for workers in lowwage occupations. Third, changes in the occupational homogeneity of workplaces are an 

important contributor to growing wage inequality among workers in the lower 98.5% of the 

wage distribution over this period. The growing separation of workers in low-wage occupations 

into different employers from workers in high-wage occupations is an important part of wage 

inequality growth.

I am grateful to seminar participants at the American Economic Association 2014 Annual 

Meetings, the University of Maryland, the Economic Policy Institute, the Federal Reserve Board 

of Governors, the Society of Labor Economists 2016 and 2017 Annual Meetings, the Center for

Equitable Growth, the NBER 2018 Summer Institute, the BRIQ Institute, and the Bureau of 

Labor Statistics for comments, with special thanks to Matt Dey, Katharine Abraham, David 

Autor, Erica Groshen, Sue Houseman, David Levine, Anne Polivka, Alex Bryson, David 

Piccone, David Weil, Nicole Fortin, Fernando Rios-Avila, and anonymous referees for 

particularly helpful comments and suggestions. This paper grew out of a project begun jointly 

with James Spletzer, and I thank him for his many contributions. Research assistance was 

provided by Lowell Mason. Any opinions and conclusions herein are those of the author and do 

not necessarily reflect the views or policies of the U.S. Bureau of Labor Statistics.
3

I. Introduction

Growing inequality of wages, particularly between employers, has been a key feature of 

the labor market in recent decades. Many changes in the labor market have been examined as 

potential sources of this inequality growth—including the decline of manufacturing, the role of 

technology in replacing employer demand for routine work, and the increased potential for 

imported goods and services to replace domestic production. This paper examines an additional 

source of growing wage inequality: the changing distribution of occupations between employers

as the organization of production changes, with employers retaining certain types of work within 

the workplace and outsourcing other work.

Much evidence shows that establishments play an important role in determining 

individual wages, beyond the role of individual workers’ characteristics (Groshen 1991a, 1991b; 

Bronars and Famulari 1997; Abowd, Kramarz, and Margolis 1999; Lane, Salmon, and Spletzer 

2007; Card, Heining, and Kline 2013). Several authors have used employer microdata to study 

growing variability in earnings in the U.S. from the mid-1970s to the early 2000s, and have 

found it due more to variation between establishments than to variation within establishments 

(Davis and Haltiwanger 1991; Dunne, Foster, Haltiwanger, and Troske 2004; Barth, Bryson, 

Davis, and Freeman 2016; Handwerker and Spletzer 2016; and Song, Price, Guvenen, Bloom, 

and von Wachter 2016),

1 while the increased sorting of high-paid workers to high-paying 

employers drives much of the growth in pay inequality between employers (Song, Price, 

Guvenen, Bloom, and von Wachter, 2016). The results in this paper show that occupational 

homogeneity—a specific form of worker sorting—is a key explanation for this growth in 

between employer wage inequality. More and more workers in low-wage occupations are 

employed in different workplaces from workers in other occupations, exacerbating differences in 

their pay.

The intersection of growing underlying wage inequality and the business environment in 

the United States can make it profitable for employers to focus on employing either low or high 

wage workers. Growing wage inequality among workers has arisen from such sources as the 

changing composition of the workforce and changing returns to education and experience,

2

the 

growing inequality within education and skill groups3, and the differential impact of technology 

on the worker skill distribution4. As wages for different kinds of work become less equal, 

employers face regulations requiring nondiscrimination across employees in the coverage of 

pension, health insurance and other benefits (EBRI 2009, Perun 2010),5increasing incentives to 

contract out work that pays very different wages from the work of other employees. Moreover, 

social norms may make it more acceptable for employers to contract out work rather than pay 

very different wages to employees doing different kinds of work (Weil 2014).

 

1 There is a large and growing literature on wage inequality growth in Europe, based on employee-employer linked 

data, including Card, Heining, and Kline (2013), who emphasize the role of increased worker sorting between 

employers in explaining wage inequality growth in Germany.

2 Bound and Johnson 1992, Katz and Murphy 1992, Lemieux 2006

3

Juhn, Murphy, and Pierce 1993, Katz and Autor 1999

4

Juhn, Murphy, and Pierce 1993, Acemoglu 2002, Autor, Katz, and Kearney 2006, 2008

5 Perun (2010) lists a variety of employment benefits which receive favorable tax treatment and are required to be 

available to low-wage as well as high-wage employees of each employer.
4

Other potential reasons for businesses to outsource work include increasing ability to 

smooth workload, economies of scale available to providers of specialized services (Abraham 

and Taylor, 1996), or a focus on “core competencies” enabled by technologies for specifying and 

monitoring work done by outsiders (Weil 2014). However, to the extent that labor cost savings 

and avoidance of efficiency wages or rents for occupations with low wages in the labor market 

are key reasons for outsourcing, it can lead to employers specializing in high or low-wage work, 

and result in growing wage inequality between establishments. Goldschmidt and Schmeider 

(2015) show labor cost savings to be a primary reason for outsourcing in Germany, as outsourced 

workers lose firm-specific rents. In three well-defined occupational categories, they find that 

losses of these firm-specific rents can account for 9% of all growth in German wage inequality 

from 1985 to 2008.

In U.S. data, direct measures of outsourcing are not generally available. Researchers have 

instead focused on particular industries or occupations associated with performing support tasks 

for other businesses. Dey, Houseman, and Polivka (2010) show a marked increase in various 

measures of outsourcing in recent years such as trends in temporary help or employment 

services. Estimates from several sources show these industries roughly doubling in size from 

1992 to 2002. They also document an increase in the employment share of occupations 

associated with outsourced labor services, such as school bus and truck drivers in the 

transportation industry and accountants in the business services industry. Yet these measures 

only capture a fraction of outsourcing—that which occurs in these specific industries. Dube and 

Kaplan (2010) use individual-level data to show the impact of outsourcing on wages and benefits 

for janitors and guards, but again, their measures can only capture outsourcing of a narrow set of 

occupations.

This paper develops economy-wide measures of outsourcing, using the homogeneity of 

occupations by employer, as measured in the detailed microdata of the Occupational 

Employment Statistics Survey conducted by the Bureau of Labor Statistics. These measures 

distinguish between two types of outsourcing, which may have differing impacts on wage 

inequality. When businesses outsource work to avoid monitoring, hiring, or other costs for 

occupations in which they have less expertise, there will be less variety overall in the 

occupations they employ. However, when businesses outsource work to narrow the wage 

distribution of their employees, the variance of wages predicted from the particular set of 

occupations they employ will decrease. The impact of the changing distributions of occupations 

and of employer occupational homogeneity are compared with the effects of other changes in 

employer characteristics (industry, size, and location) on the overall distribution of wages.

There are three major findings. First, from November 2002 through November 2016, the 

occupational homogeneity of employers increased for workers in typically low-wage 

occupations, after controlling for other employer characteristics. Second, wages are related to the 

occupational homogeneity of establishments. Workers in more occupationally homogeneous 

establishments earn lower wages. This relationship holds even after controlling for workers’ own 

occupations and observable characteristics of their employers, and is strongest for workers in 

occupations typically paid low wages. Third, changes in the distribution of this occupational 

homogeneity are related to the growth in private-sector wage inequality observed in the data 

during this time period. Nearly a substantial amount of the growth in ln(wage) variance, as 
5

measured in the OES data, can be attributed to the growing occupational homogeneity of

establishments over this period. Among the measures of employer homogeneity, a measure based 

on the distribution of occupations by wage levels matters more for the growth in wage inequality 

than a more functional measure of employer homogeneity that ignores wage differences among

occupations.

The paper is organized as follows: Section II describes measures of occupational homogeneity. 

Section III describes trends in measured occupational homogeneity of employers. Section IV

describes relationships between employer occupational homogeneity and employee wages.

Section V describes the impact of the changing distributions of occupation and the occupational 

homogeneity of employers on wage inequality over time. Section VI concludes.

II. Measuring the Occupational Homogeneity of Employers

I use the term “occupational homogeneity”

6

to describe the variety of occupations 

employed at a place of business, separate from the tasks performed by individual employees 

(their occupations), the type of work done at the business (its industry) or the size of the 

business. Much scholarship on outsourcing (for example Dey, Houseman, and Polivka, 2010; 

and Erickcek, Houseman, and Kalleberg, 2003) examines particular occupations and particular 

industries. In contrast, occupational homogeneity is intended as a measure of the variation in 

work done in all businesses, through the full range of industries in the economy. This section 

defines two measures of occupational homogeneity and presents evidence showing that these 

measures are related to examples in the outsourcing literature.

The two measures of the occupational homogeneity of establishments are very different: 

(1) a measure involving the overall distribution of occupations, regardless of whether they are 

high or low paid, and (2) a measure that explicitly models the variation in wages of 

establishments due to the distribution of occupations employed. 

The first measure of occupational homogeneity for establishment j at time t is constructed with a 

Herfindahl-Hirschman index of employment, n, in each occupation k within that establishment:

(1) Hjt = ∑ (

nkjt

njt )

2

100

k=1

This index uses the 100 minor occupational categories at the 3-digit level of the Standard 

Occupational Classification system.7It varies from 1/100 (equal representation of all 

occupations) to 1 (complete homogeneity). Increased occupational homogeneity at the 

establishment level by this measure indicates that employers are becoming more specialized, 

consistent with outsourcing work to other employers. Trends in this measure indicate whether 

 

6 Earlier versions of this paper referred to the same concept as “occupational concentration.”

7 Handwerker and Spletzer (2016) studied this type of general occupational homogeneity with HerfindahlHirschman indices, using both the detailed 6-digit occupations of the Standard Occupational Classification System 

(829 categories) and the 2-digit major occupational categories of the Standard Occupational Classification System 

(22 categories), and found very similar time trends and relationships between occupational classification and wages

with broad and detailed versions of this measure.
6

establishments throughout the U.S. economy are becoming more homogeneous in the 

occupations they employ. However, this measure cannot distinguish between specializing in a 

few occupations typically paid very different wages, such as 29-1000 (Healthcare Diagnosing or 

Treating Practitioners) and 31-1100 (Home Health and Personal Care Aides; and Nursing 

Assistants, Orderlies, and Psychiatric Aides), or specializing in a similar number of occupations

that are typically paid very similar wages.

In contrast, the second measure of occupational homogeneity is explicitly constructed to 

capture the similarity or dissimilarity of typical wages for the occupations each establishment 

employs. It is the variance of wages for each establishment that would be predicted from the

establishment’s distribution of employment by occupation, without using information on the 

actual wages paid at the establishment. Using average log wages for each minor occupational 

category in each time period, the log wage paid by employer j to worker i in occupation k at time 

t is estimated as ŵijt = w̅̅̅kt̅̅ + εijt, where w̅̅̅kt̅̅ is the mean log wage for all employees in 

occupation k at time t and εijt is distributed normally, with mean 0 and standard deviation σk.

From the occupational distribution of employer j at time t, the estimated mean log wage for j at t 

is estimated w̅̂̅̅jt̅ =

∑ ∑ wkt ̅̅̅̅̅̅ k i ∈k

njt

, where njt is the total employment for employer j at time t, and i ∈

k denotes observations in which individual i has occupation k. Again, using only the distribution 

of occupations employed and the average wages of these particular occupations across all 

employers at time t, the predicted log wage variance for employer j at time t is

(2) V̂jt =

∑ (ŵijt−̅w̅̂̅jt̅̅)

2

i

njt

= 

∑ njkt[(wkt ̅̅̅̅̅̅−̅w̅̂̅jt̅̅)

2

k ]

njt

+

∑ njktσkt2

k

njt

.

This has two terms: the variation in average wages between occupations, and the average of 

within-occupation log wage variances. The sum of these terms is the second measure of 

occupational homogeneity: the variance of log wages for employer j at time t predicted from the 

composition of the occupations employed.

Both of these measures are estimated with the microdata of the Occupational 

Employment Statistics (OES) Survey for the private sector in the United States for 2002 to 

2016.

8 These microdata record the number of employees by wage interval within detailed 

occupation categories for hundreds of thousands of establishments per year. The OES survey is 

designed to produce estimates of employment and wages in the United States for each detailed 

occupation, by geography and industry. It covers all establishments in the United States except 

for those in agriculture, private households, and unincorporated self-employed workers without 

employees. It is the only survey of its size and scope.

The OES collects data for a sample of about 200,000 establishments each November and 

each May. Sampled establishments are asked to report the number of employees in each 

occupation by wage interval. As described in Dey and Handwerker (2016), the OES uses a 

complex sample design intended to minimize the variance of published wage estimates for each 

occupation within industries and geographic areas. Establishments expected to employ rarer 

 

8 An earlier version of this paper used microdata for 1999 to 2015. However, as described by Abraham and Spletzer 

(2010), many first-line supervision occupations in establishments of less than 500 workers were erroneously coded 

as managerial occupations during 1999-2001. Thus, microdata for earlier years have now been dropped. 
7

occupations or occupations with greater variation in wages have relatively larger probabilities of 

selection.

In using OES data to study wage inequality, it is important to understand that the OES 

data cannot measure inequality in the topmost percentiles of the wage distribution. Wages are 

reported to the OES in intervals. The OES program uses the mean of each wage interval every 

year from the National Compensation Survey (NCS) to assign wages for employees in each 

interval. Earnings of individuals at the very top of the wage distribution are topcoded in the 

OES—the uppermost interval in the recent OES surveys is “$208,000 and over.” Averaged 

across all years, the uppermost interval contains roughly 1.3 percent of employment.

Handwerker and Spletzer (2014) compare wage inequality levels and trends in these same

reweighted OES microdata with the wage inequality level and trends in the outgoing rotation 

group microdata of the CPS, which has been used in many of the most cited studies of wage 

inequality. They show the interval nature of wage collection in the OES has almost no impact on 

overall wage variance trends. The reweighted OES data broadly replicate the CPS wage 

distribution trends: overall wage variances in each year are similar in the reweighted OES and 

CPS microdata, as well as overall variance trends, and variance trends by sector, industry groups, 

and occupation groups.

The OES sample design uses 3 years, or 6 panels of data collection, to produce detailed 

published estimates of employment and wages. It is not designed to produce time series 

estimates of either employment or wages for any individual occupation. This paper uses data 

reweighted using the methodology of Abraham and Spletzer (2010) to match the detailed 

industry and employer size distribution of the Quarterly Census of Employment and Wages for 

each May and November. 

Establishments are the sampling units of the OES, and so this paper focuses on measures 

of occupational homogeneity at the establishment level. However, all the main results in this 

paper have been repeated with measures constructed at the Employer Tax-ID level (EIN), with 

little impact on the results (EIN-level results are shown in Appendix C). 

The Data Appendix contains summary statistics, including the composition of 

occupations and industries. The average worker has an inflation-adjusted wage of $16.20/hour, 

or a ln(wage) of 2.56, and is observed in an establishment with a measured ln(wage) variance of 

0.154. The average Hirfindahl-Hirschman index for workers’ establishments is 0.408, and the 

average predicted variance of ln(wages) estimated from its workers’ occupational composition is 

0.270. It is unsurprising that the predicted ln(wage) variance based only on the occupations 

employed at the establishment is higher than the measured ln(wage) variance because of the 

large literature describing the impact of employer-specific factors on wages.

Table 1 compares the two measures of establishment-level occupational homogeneity for 

several occupation-industry groups studied as examples of outsourcing by Abraham and Taylor 

(1996); Dube and Kaplan (2010); Dey, Houseman, and Polivka (2010); Weil (2014); and 

Goldschmidt and Schmeider (2015): the entire food preparation and serving major occupational 

group, janitors, security guards, truck drivers, accountants, computer occupations, engineering 

occupations, and lawyers. Outsourcing of workers in these occupations means that they are 
8

employed in the specialty industries of food services, janitorial services, security guard services, 

truck transportation, accounting services, computer services, engineering services, or law offices, 

rather than the industry of the business to which they provide these services. Table 1 shows that 

for every single one of these example occupations or occupation groups, the HerfindahlHirschman indices for employers of these workers (as defined in equation (1)) are higher, on 

average, indicating greater occupational homogeneity of employers, when they are employed in 

their specialty industry than when they are employed in other industries. Moreover, for every 

example occupation except lawyers (the smallest, highest paid, and employed by the most

occupationally homogeneous employers in its specialty industry), the predicted variances of 

wages based on the occupational distribution of their employers are lower, on average, indicating 

greater occupational homogeneity of employers, when they are employed in their specialty 

industry than when they are employed in other industries. Both measures of occupational 

homogeneity measures defined in this section—designed to measure outsourcing across all 

occupations and industries—indicate greater occupational homogeneity in the relevant industries 

to which workers are outsourced for the specific occupations studied in the outsourcing casestudy literature.

III: Trends in Occupational Homogeneity Measures

Understanding trends in occupational homogeneity measures is complicated by 

contemporaneous changes in the overall occupational composition of the labor force. As 

described by Autor, Katz, and Kearney (2006, 2008), among others, employment in typically 

low-wage and typically high-wage occupations has increased, while employment in many 

typically middle wage-occupations has decreased. Figure 1 shows employment over time for 

occupational quintiles in the OES. Employment polarization is clear in the OES data: there is an 

increasing fraction of employment over time in the top and bottom quintiles, with a decreasing 

fraction of employment in the middle three quintiles. This polarization means that if we entirely 

ignore the grouping of employment into establishments, the portion of the variance of ln(wages) 

for all workers due to wage variation between occupations is generally increasing—from values 

varying between 0.20 and 0.21 in the early years of the microdata to values varying between 0.22 

and 0.23 in later years. In other words, the overall polarization of employment mechanically 

leads to increases in the variance of wages between occupations. There is no such mechanical 

relationship between overall changes in employment by occupation and the HerfindahlHirschman index: a version of the Herfindahl-Hirschman index that pools workers across all 

employers varies only between .027 and .028 over this period, with no clear time trend. 

The actual time trend of mean occupational homogeneity at the establishment level is 

described with regressions of the form 

(3) OccHomogeneityijt = αSurvey Datet + βI(May Surveyt

) + γXijt + εijt

where SurveyDate measures time in decades since November 2002, I(May Survey) is an 

indicator to capture seasonal variation between data collection in May and November, and Xijt

are other observable characteristics of individual i (occupation) and employer j (industry, 

geography, and size) at time t. Trend regression results for equation (3) are shown in Tables 2
9

and 3. The first two rows of Table 2 show an increase over time in the Herfindahl-Hirschman

measure of the occupational homogeneity of employers overall, but changes in occupations and 

employer characteristics explain about 95% of this increase. Meanwhile, the predicted variance 

of ln(wages) measure of occupational homogeneity has risen over time, showing a trend of 

decreasing employer homogeneity overall for this measure.

Further rows of Table 2 repeat this analysis for subgroups of occupations. Occupations 

(at the 3-digit minor occupational category SOC level) are grouped by average wage into 

quintiles, with roughly equal total weighted employment in each quintile. 9 Appendix A lists the 

occupations of each quintile, while counts of the individual and employer observations for each 

quintile are in the Data Appendix. The list of occupations in the lowest-paid quintile is a short 

one, because the occupations in this quintile tend to be large, such as Food and Beverage Serving 

Workers. The list of occupations in the highest-paid quintile is much longer, because these 

occupations tend to be smaller, such as Social Scientists. 

The subgroup rows of Table 2 show the greatest increases over time in the HerfindahlHirschman measure of occupational homogeneity—after including controls for occupation and 

establishment characteristics—occur in the bottom and top quintiles of occupations. For the 

predicted variance measure of occupational homogeneity, there is an increase over time for the 

top three higher-paid quintiles of occupations that persists after including the controls, and a 

decrease in variance over time (showing greater employer homogeneity) that persists after 

including the controls for the occupations in the lowest-paid quintile.

The trend regressions of Table 2 measure changes only in the mean levels of these 

occupational homogeneity measures. In overall distributions of the two measures of occupational 

homogeneity, the Herfindahl-Hirschman measure has a similar shape over time. However, the 

distribution of values for the predicted variance of ln(wages) becomes increasingly bimodal over 

time, with modal values falling for establishments employing people in typically low-wage 

occupations and rising for employers of higher-wage occupations.

Figure 2 uses the same five quintiles of occupations by typical wages used in Table 2, 

and shows the fraction of workers in each quintile of occupations who work in establishments 

without any workers in other quintiles. It is unsurprising that workers in all other quintiles of 

occupation are growing less likely to have any coworkers in the middle three quintiles, as the 

middle quintile occupations have declining shares of overall employment over time. However, 

Figure 2 shows that workers in the bottom three quintiles increasingly have no coworkers in the 

top quintile of occupation, and workers in the top three quintiles increasingly have no coworkers 

in the bottom quintile of occupation, although the bottom and top quintiles of the occupational 

distribution have increasing shares of overall employment over time.

This figure helps to explain how the predicted-variance measures can be falling 

(increased homogeneity) for workers in typically low-wage occupations while the same 

measures can be rising (increased heterogeneity) for workers in typically high-wage occupations.

The polarization of overall employment is increasing, with rising shares of employment in the 

 

9 To form quintiles, occupations are ranked by their average wages across all years. This grouping of occupations is 

quite stable over time.
10

top and bottom quintiles of occupations at the expense of the middle three quintiles of 

occupations. However, this polarization is not happening evenly across establishments.

To illustrate the impact of these trends in employment by occupational quintiles on the 

predicted variance of wages for establishments, consider coarsening the occupational distribution 

into only three occupation groups: low-wage occupation group L, middle-wage occupation group 

M, and high-wage occupations H, with mean wages for occupations in each group w̅̅̅L̅ < w̅̅̅M̅ < 

w̅̅̅H̅ and within-occupations wage variances by group σL

2 < σM2 < σH2

. Each establishment j 

contains nL ≥ 0 workers in the low-wage occupation group, nM ≥ 0 workers in the middle-wage 

occupation group, and nH ≥ 0 workers in the high-wage occupation group, with nL + nM +

nH = nJ. The predicted variance of wages for each establishment is Vj

̂ = 

nL[(w̅̅̅L̅−̅ŵ̅̅j̅)

2

]

nj

+

nM[(w̅̅̅M̅̅−̅ŵ̅̅j̅)

2

]

nj

+

nH[(̅w̅̅H̅̅−̅ŵ̅̅j̅)

2

]

nj

+

nLσL

2

nj

+

nMσM

2

nj

+

nHσH

2

nj

.

For workers in occupation group L, employing establishments have higher nL, lower nM, 

and lower nH, and, as shown in Figure 2, growing numbers of workers in occupation group L 

work in establishments with nM = nH = 0. There is little variation in wages between the low-wage 

occupations (a low value of σL

2

), which reduces the typical values of (w̅̅̅L̅ − w̅̂̅̅j). With fewer 

workers in middle or high wage occupations, there is less weight on the other components of the 

predicted wage variance. This lowers Vj

̂ for the workers in occupation group L.

For workers in occupation group H, employing establishments have lower nL, lower nM, 

and higher nH, and, as shown in Figure 2, growing numbers of workers in occupation L work in 

establishments with nL = nM = 0. Although average wages are higher in these establishments, 

reducing the typical values of (w̅̅̅H̅ − w̅̂̅̅j), the greater weight nH associated with the high wage 

variance within these occupations, σH

2

, means a greater Vj

̂ overall for these establishments.

To understand how a high-paying occupation can have growing occupational 

homogeneity over time by the Herfindahl-Hirschman measure at the same time it has shrinking 

occupational homogeneity over time by the predicted wage variance measures (increased 

variance), consider accountants, an example high-wage occupation from Table 1. During this 

period, the share of accountants working in the manufacturing sector fell while the share working 

in the management of companies sector rose. Establishments that employed accountants became 

increasingly specialized in two occupational categories: Health Diagnosing and Treating 

Practitioners and Business Operations Specialists, with some growth in the number of Financial 

Specialists, the category that includes accountants. All of these are high-wage occupational 

categories. Meanwhile, employers of accountants employed fewer people in occupational 

categories such as Other Office and Administrative Support Workers, Production occupations, 

Secretaries and Administrative assistants, and Financial Clerks. This changing occupational 

distribution for employers of accountants meant rising predicted wage variances (more 

heterogeneity by wage variance measures), even as these employers concentrated employment in 

particular occupations (greater homogeneity by the Herfindahl-Hirschman measure).
11

Weil (2014) describes how large corporations have shed many low-wage tasks by 

outsourcing them to other companies, which repeatedly subcontract them to smaller and smaller 

employers. Figure 3 shows that establishment size plays a role in the increasing segregation of 

workers in the lowest-paid quintile of occupations and workers in the highest-paid quintile of 

occupations into separate establishments, following the pattern Weil describes. Rising shares of 

employment for the lowest-paid quintile of occupations occurred only in establishments of less 

than 100 workers, while rising share of employment for the highest-paid quintile of occupations 

occurred more sharply in establishments of 100 or more workers.10

Table 3 shows the implication of this growing segregation of workers by establishment 

size for time trends in measured establishment occupational homogeneity. This table 

disaggregates the results of equation (3) by both establishment size and occupation group. For 

workers in the lowest-paid quintile of occupations in establishments with less than 100 workers, 

including the controls described above, workplaces are increasingly homogenous, by both 

measures. Furthermore, the predicted variance of ln(wages) measure of occupational 

homogeneity has fallen faster (homogeneity has increased more) for low-paid workers in these 

smaller establishments than in establishments with 100 or more workers.

Appendix B describes employer occupational homogeneity trends along several other 

dimensions—by state-level unionization rates, more detailed establishment size categories, 

establishment age, industrial sector, and Employer Tax Identification Number (EIN) size.

For workers in low-wage occupations, all measures show a clear trend of increased

employer occupational homogeneity over time. The next section shows these workers’ wages are

lower when they work for more occupationally homogenous employers. 

IV: Relationships between Measured Occupational Homogeneity and Wages

The outsourcing literature provides several examples of occupations in which outsourcing 

is associated with lower wages, including occupations listed in Table 1. Among the example 

occupations in Table 1, all of the low wage occupations (food preparation and service, janitors, 

and security guards) earn considerably lower average wages in outsourced specialty industries

than in other industries. These example occupations are examples precisely because there are 

obvious industries to which they can be outsourced; most other occupations do not have such 

obvious industries for outsourcing. However, the advantage of occupational homogeneity is that 

is can be measured for the employers of all occupations. This section shows the relationship 

between occupational homogeneity and wages for all workers. 

I describe the relationship between occupational homogeneity and wage with regressions

of the form

(4) ln(wageijt) = αOccHomogeneityjt + βDatetOccHomogeneityjt + γXijt + εijt,

 

10 Patterns are similar for establishments of 1-49 workers and establishments of 50-99 workers. Patterns are also 

quite similar when using EIN size instead of establishment size.
12

where OccHomogeneityjt is the measure of occupational homogeneity for the employer of 

individual i at employer j in time t, Datetis the date, and Xijt are other observable 

characteristics of individual i (occupation) and employer j (industry, geography, and size) at time 

t. Results of this regression are shown in Table 4. The first row of this table gives estimates of 

the impact of occupational homogeneity on wages, α, with no additional variables. These 

estimates show that increased occupational homogeneity is associated with lower wages overall.

Estimates of the coefficients  (not shown) indicate that all these relationships have significantly 

strengthened over time. The second row of Table 4 gives these estimates with additional 

variables added to the regression. These detailed controls reduce the magnitude of the 

relationship between occupational homogeneity and wages, α, but the estimates maintain the 

same sign and remain very significant.

Further rows of Table 4 repeat this analysis for the same subgroups of occupations as in 

Table 2. The relationship between occupational homogeneity and wages, for both measures of 

occupational homogeneity, is generally stronger for workers in typically low-wage occupations 

than for workers in typically high-wage occupations. The relationship between the typical wage 

levels for a quintile of occupations and the wage coefficient of occupational homogeneity for the 

occupations in that quintile is not monotonic, with the largest wage coefficients are generally for 

the quintile of occupations with the second-lowest typical wages. For workers in the highest-paid 

quintile of occupations, greater occupational homogeneity is associated with higher wages by 

both measures of occupational homogeneity, once industry, own-occupation, geography and 

employer size are taken into account.

Estimates of  (not shown) for all occupations, with full occupation and employer 

controls, have the opposite sign to the estimates of α, indicating that the impact of occupational 

homogeneity on wages for all occupations has been decreasing over time overall. However, the 

regressions by quintile of occupation show that these estimates of  reverse for the middle-wage 

occupations, indicating that the relationship between occupational homogeneity and wages has 

been growing over time for these occupations.

Appendix B describes further heterogeneity in the relationship between occupational 

homogeneity and wages—by state-level unionization rates, establishment size, establishment 

age, industrial sector, and Employer Tax Identification Number (EIN) size.

This section has described the relationship observed between occupational homogeneity 

and wages, but does not say employer homogeneity “causes” lower wages for workers in lowerwage occupations. The data used in this paper do not allow me to measure whether differences in 

unmeasured skills and tasks—within the same occupation—might explain some of the difference 

in wages between workers in more and less homogenous workplaces. For example, janitors who 

work in the janitorial services industry may lack some specialized skills of janitors in other 

industries, and may perform somewhat different tasks than those employed in other industries. 

However, the many U.S. examples described in Weil (2014) and the labor force histories of 

German workers whose jobs are outsourced, as documented in Goldschmidt and Schmieder 

(2015) provide evidence that some portion of the observed relationship between employer 

homogeneity and wages is causal. The estimates in thus section should thus be considered an 

upper bound for the size of the causal impact of employer homogeneity on wages.
13

V. Occupational Homogeneity and Wage Inequality

The association between occupational homogeneity and lower wages for workers in 

lower-wage occupations, coupled with the trend of growing occupational homogeneity for 

workers in the lowest-wage occupations, suggests a role for occupational homogeneity in 

explaining growing wage inequality. Barth, Bryson, Davis, and Freeman (2016) highlighted that 

most inequality growth is between establishments, and is not explained by industry or 

geography. Moreover, Song, Price, Guvenen, and von Wachter (2016) show that the vast 

majority of pay-inequality growth at small and medium-sized firms from 1978-2013 was due to 

increasing segregation and sorting of workers who earn higher pay—without describing what 

about these workers makes them higher-paid workers—to firms that pay higher wages. Weil 

(2014) speculated that increased fissuring of employers could exacerbate wage inequality, but he 

did not have data to measure this directly. This section presents evidence showing that changes 

in occupational homogeneity contribute to the growth in wage inequality during this period.

I use Rios-Avila’s implementation of Fortin, Firpo, and Lemiuex’s Recentered Influence 

Function (RIF) Decomposition method to decompose changes in real ln(wage) variance from 

Nov 2002 & May 2003 to Nov 2014 & May 201511 into portions that can be explained by the 

changing composition of workers by occupation, and the changing composition of their 

employing establishments by industry, geography, size, and occupational homogeneity. Because 

the occupational homogeneity measures are continuous rather than categorical variables, these 

variables are divided into quartiles for this reweighting exercise.12 The evidence in Table 2 

shows that occupational homogeneity is changing in different ways for different quintiles of 

occupations. Thus, I interact occupational homogeneity variables with the same quintiles of 

occupation used above.

13

 In addition, I add a dummy variable for lowest-wage quintile 

occupations employed establishment of less than 100 workers that are in the bottom half of the 

predicted variance distribution to the vector of indicator variables describing the predicted 

variance measure of occupational homogeneity.

Results are shown in Table 5. The changing composition of employment by industry, 

geography, establishment sizes, occupational quintiles, and the categories of occupational 

homogeneity described above can more than explain all of the change in ln(wage) variance from 

2002-2003 to 2014-2015. The specification error is only 2% of this amount. Decomposing the 

change in ln(wage) variance by source, only tiny amounts are explained by changes in 

employment by industry, geography, establishment size, and the occupational homogeneity of 

 

11 The November 2014 – May 2015 period is chosen as the end date because overall wage variance in lower in the 

2016 panels than in the 2015 panels for the unimputed OES microdata. Results for the later end date are in Appendix 

D. 

12 Quartiles are chosen for the main results here so that the interaction between occupational homogeneity quartiles 

and occupational quintiles is 20 categories, roughly the same number of categories as the number of industrial 

sectors. If I instead divide the measures of occupational homogeneity into deciles, and use the same detailed levels 

of industry and occupation variables as in Tables 2-4, occupational homogeneity by the predicted variance of wages 

measure and its interaction with occupational quintiles explain a similar amount of wage variance growth. Results 

are shown in Appendix D.

13 This follows the example of Goldschmidt and Schmieder (section V.C.), who use indicators for deciles of the firm 

wage effect interacted with dummies for frequently outsourced occupations.
14

employers by the Herfindahl-Hirschman measure. In contrast, 87% of the change in ln(wage) 

variance can be explained by changes in the occupational quintiles of workers (the polarization 

of employment), and 11% of the growth in ln(wage) variance is explained by changes in the 

occupational homogeneity of employers by the predicted variance of wages for the occupations 

they employ.

To examine the impact of changing occupational homogeneity on the growth of wage 

variance between establishments, I use the Dinardo-Fortin-Lemiuex (DFL) 1996 method. This 

method calculates counterfactual wage distributions by reweighting observable characteristics in 

the later period to their distributions in the earlier period. In this exercise, increased wage 

inequality growth from the November 2002 – May 2003 period to the November 2014 - May 

2015 period can be attributed to changes in the distribution of employment by reweighting the 

data in the later period so that characteristics have the same distribution that they did in the 

earlier period. The DFL method cannot attribute changes in the wage distribution to the amounts 

attributable to changes in each characteristic as the FFL method does, but producing this 

counterfactual distribution of wages makes it simple to examine the impact of this change on the 

between-establishment wage variance.

The overall variance of real ln(wages) increased from 0.375 in 2002-2003 to 0.400 in 

2014-2015, and essentially all of this increase is due to between-establishment wage variance 

increasing from 0.219 to 0.243. Reweighting the 2014-2015 data to the 2002-2003 distribution of 

employment by quintiles of the occupational distribution, the predicted variance of establishment 

ln(wages), this predicted variance interacted with occupational quintiles, and an indicator for 

workers in typically low-wage occupations employed in small homogenous establishments, the 

between-establishment wage variance would be .232 rather than the actual .243. This 

reweighting can thus explain 47% of wage variance growth between establishments.

In sum, these results show that changes in occupational homogeneity are a very important 

part of growing wage inequality for the lower 98.5% of the wage distribution. Furthermore, the 

predicted variance of wages measure matters much more in explaining the growth in wage 

inequality than Herfindahl-Hirschman measure. The separation of typically-low wage 

occupations into separate workplaces from typically-high wage occupations is the form of 

occupational homogeneity that matters for wage inequality. 

VI. Summary: Outsourcing and increasing wage inequality

While many authors have studied the growth in wage inequality between employers and 

others have studied the impact of outsourcing on wages in particular occupations and industries, 

this paper is among the first to connect the two with a study of the impact of the changing 

distribution of occupations between employers on wage inequality in the United States. This 

paper uses multiple measures of occupational homogeneity (at both the establishment and 

employer tax-ID levels) to examine the impact of outsourcing on wages and on wage inequality.

These measures show greater occupational homogeneity for the occupations used to study 

outsourcing by Abraham and Taylor (1996); Dube and Kaplan (2010); Dey, Houseman, and 

Polivka (2010); and Goldschmidt and Schmeider (2015), when these occupations are employed 
15

in establishments in the outsourced sector. For example, employer occupational homogeneity is 

higher for janitors when they are employed in establishments in the janitorial services industry 

than when they are employed in other industries.

The advantage of measuring outsourcing with occupational homogeneity is that these 

measures can be calculated for every employee of every employer, not only for “case study” 

occupations. This paper shows that by two very different measures of occupational 

homogeneity—for employers of every size—there is an increase in employer homogeneity over 

time for the quintile of workers in the lowest-wage occupations. Falling employment levels for 

middle-wage occupations mean those in other occupations have fewer coworkers in middle-wage 

occupations, but low-wage workers also have a declining share over time of coworkers in highwage occupations, even as low-wage and high-wage occupations make up a growing share of 

employment. Low-wage occupations are growing in smaller employers, while the growth of 

high-wage occupations is concentrated in large employers. These patterns of time trends are

consistent with the idea that in the economy as a whole, companies are “de-verticalizing” by 

outsourcing functions not part of their “core competencies,” particularly if these outsourced tasks 

are done by workers paid lower wages than the “core workers” in the establishment. 

The paper further shows that employer occupational homogeneity is related to wage 

levels. It has a particularly strong negative wage association for workers in occupations that are 

typically low paid, even after controlling for the occupations of employees and various 

observable characteristics of their employers. In contrast, workers in the highest paid quintile of 

occupations have a strong positive association with employer occupational homogeneity after 

controlling for their own occupations and the observable characteristics of their employers.

Song, Price, Guvenen, and von Wachter (2016) show that the vast majority of payinequality growth at small and medium-sized firms is due to the increasing segregation and 

sorting of workers who earn lower pay—without describing what about these workers makes 

them lower-paid workers—to firms that pay lower wages. Occupation is just such a characteristic 

affecting workers’ wages, and this paper shows that workers in low-wage occupations are 

increasingly concentrated at employers with fewer high-wage occupations, contributing to wage 

inequality growth.

Although the data used in this paper cannot show changes in the wage distribution for the 

very highest 1.5% of wage-earners, they are well suited to measure the contribution of 

employers’ occupational homogeneity to wage inequality growth for the remaining 98.5% of the 

wage distribution. Decompositions of ln(wage) variance growth in these data show very little 

role for the changing composition of employment by industry, geography, and establishment 

size. However, the growing polarization of employment can explain the vast majority of 

inequality growth, and the changing distribution of occupational homogeneity by the typical 

wage level of occupations can explain nearly all of the remainder. Growing separation of 

workers in low-wage occupations from the employers of workers in high-wage occupations is an 

important component of recent wage inequality growth.
16

REFERENCES

Abowd, John M., Francis Kramarz, and David Margolis (1999). “High Wage Workers and High 

Wage Firms.” Econometrica, pp. 251-334.

Abraham, Katharine G. and James R. Spletzer (2009). “New Evidence on the Returns to Job 

Skills.” American Economic Review Papers and Proceedings, Vol. 99, No. 2, pp. 52-57.

Abraham, Katharine G. and James R. Spletzer (2010). “Are the New Jobs Good Jobs?,” in Labor 

in the New Economy, edited by Katharine G. Abraham, James R. Spletzer, and Michael 

Harper. University of Chicago Press.

Abraham, Katherine G and Susan K. Taylor (1996). “Firms’ use of Outside Contractors: Theory 

and Evidence.” Journal of Labor Economics, 14(3): 394-424.

Acemoglu, Daron (2002). “Technical Change, Inequality, and the Labor Market.” Journal of 

Economic Literature, Vol. 40, No. 1, pp. 7-72.

Autor, David, H., and David Dorn (2013). “The growth of low-skill service jobs and the 

polarization of the US labor market.” The American Economic Review, Vol.103, No. 5, pp.

1553-1597.

Autor, David H., Lawrence F. Katz, and Melissa S. Kearney (2006). “The Polarization of the 

U.S. Labor Market,” American Economic Association Annual Meeting Papers and 

Proceedings, pp. 189-194.

Autor, David H., Lawrence F. Katz, and Melissa S. Kearney (2008). “Trends in U.S. Wage 

Inequality: Re-Assessing the Revisionists.” Review of Economics and Statistics, Vol. 90, No. 

2, pp. 300-323.

Barth, Erling, Alex Bryson, James C. Davis, and Richard Freeman (2016). “It's Where You 

Work: Increases in Earnings Dispersion across Establishments and Individuals in the U.S.”

Journal of Labor Economics, 34(S2): S67-S97.

Bound, John and George Johnson (1992). “Changes in the Structure of Wages in the 1980's: An 

Evaluation of Alternative Explanations.” The American Economic Review, Vol. 82, No. 3, 

pp. 371-392.

Bronars, Stephen G. and Melissa Famulari (1997). “Wage, Tenure, and Wage Growth Variation 

Within and Across Establishments.” Journal of Labor Economics, pp. 285-317.

Card, David, Jorg Heining, and Patrick Kline (2013). “Workplace Heterogeneity and the Rise of 

West German Wage Inequality.” Quarterly Journal of Economics, Vol. 128, No. 3, pp. 967-

1015.
17

Dey, Matthew, and Elizabeth Weber Handwerker (2016). "Longitudinal data from the 

Occupational Employment Statistics survey." Monthly Labor Review.

Dey, Matthew, Susan Houseman, and Anne Polivka. 2010. "What Do We Know about 

Contracting Out in the United States? Evidence from Household and Establishment 

Surveys." In Labor in the New Economy, Katharine G. Abraham, James R. Spletzer, and 

Michael J. Harper, eds. Chicago: University of Chicago Press, 2010, pp. 267-304

Davis, Steve J. and John Haltiwanger (1991). “Wage Dispersion Between and Within U.S. 

Manufacturing Plants.” Brookings Papers on Economic Activity, pp. 115-200.

DiNardo, John, Nicole M. Fortin, and Thomas Lemieux (1996). “Labor Market Institutions and 

the Distribution of Wages, 1973-1992: A Semiparametric Approach.” Econometrica, Vol. 

64, No. 5, pp. 1001-1044.

Dube, Arindrajit, and Ethan Kaplan (2010). "Does outsourcing reduce wages in the low-wage 

service occupations? Evidence from janitors and guards." ILR Review 63.2: 287-306.

Dunne, Timothy, Lucia Foster, John Haltiwanger, and Kenneth R. Troske (2004). “Wage and 

Productivity Dispersion in United States Manufacturing: The Role of Computer Investment.”

Journal of Labor Economics, Vol. 22, No. 2, pp. 397-429.

Employee Benefit Research Institute (EBRI) (2009). Fundamentals of Employee Benefit 

Programs, 6th ed. http://www.ebri.org/publications/books/index.cfm?fa=fundamentals

Erickcek, George, Susan Houseman, and Arne Kalleberg (2003). “The Effects of Temporary 

Services and Contracting Out on Low-Skilled Workers: Evidence from Auto Suppliers, 

Hospitals, and Public Schools,” in Low-Wage America: How Employers are Reshaping 

Opportunity in the Workplace, edited by Eileen Appelbaum, Annette Bernhardt, and Richard 

J. Murnane. New York: Russell Sage Foundation, pp.368-403.

Firpo, Sergio P., Nicole M. Fortin, and Thomas Lemieux (2018). "Decomposing Wage 

Distributions Using Recentered Influence Function Regressions." Econometrics 6 (3):41.

Fortin, Nicole, Thomas Lemieux, and Sergio Firpo (2011). “Decomposition Methods in

Economics,” Handbook of labor economics 4 (2011): 1-102.

Goldschmidt, Deborah and Johannes F. Schmieder (2015). “The Rise of Domestic Outsourcing 

and the Evolution of the German Wage Structure,” NBER Working Paper #21366. The 

Quarterly Journal of Economics, forthcoming?

Goos, Maarten and Alan Manning (2007). “Lousy and Lovely Jobs: The Rising Polarization of 

Work in Britain,” Review of Economics and Statistics, Vol. 89, No. 1, pp. 118-133.

Goos, Maarten, Alan Manning and Anna Salomons (2009). “Job Polarization in Europe,” 

American Economic Review Papers and Proceedings, Vol. 99, No. 2, pp. 58-63.
18

Groshen, Erica L. (1991a). “Sources of Intra-Industry Wage Dispersion: How Much do 

Employers Matter?” The Quarterly Journal of Economics, Vol. 106, No. 3, pp. 869-884.

Groshen, Erica L. (1991b). “Five Reasons Why Wages Vary Among Employers.” Industrial 

Relations, Vol. 30, No. 3, pp. 350-381.

Handwerker, Elizabeth Weber, & Mason, Lowell G. (2013). Linking firms with establishments 

in BLS microdata. Monthly Labor Review, Vol 136, No 14.

Handwerker, Elizabeth Weber and James R. Spletzer (2014). “Measuring the distribution of 

wages in the United States from 1996-2010 with the Occupational Employment Survey” 

Monthly Labor Review, May 2014

Handwerker, Elizabeth Weber, and James R. Spletzer (2016). "The Role of Establishments and 

the Concentration of Occupations in Wage Inequality." Inequality: Causes and 

Consequences. Emerald Group Publishing Limited, 2016. 167-193.

Juhn, Chinhui, Kevin M. Murphy, Brooks Pierce (1993). “Wage Inequality and the Rise in 

Returns to Skill Wage Inequality and the Rise in Returns to Skill.” The Journal of Political 

Economy, Vol. 101, No. 3, pp. 410-442.

Katz, Lawrence F. and Autor, David H. (1999). “Changes in the Wage Structure and Earnings 

Inequality.” In Handbook of Labor Economics (volume 3A), edited by Orley Ashenfelter and 

David Card, Elsevier, pp. 1463-1555.

Katz, Lawrence F. and Kevin M. Murphy (1992). “Changes in Relative Wages, 1963-1987: 

Supply and Demand Factors.” The Quarterly Journal of Economics, Vol. 107, No. 1, pp. 35-

78.

Lane, Julia. I., Salmon, Laurie A., & Spletzer, James R. (2007). Establishment wage 

differentials. Monthly Labor Review, Vol. 130,No.3.

Lemieux, Thomas (2006). “Increasing Residual Wage Inequality: Composition Effects, Noisy 

Data, or Rising Demand for Skill?” American Economic Review, Vol. 96, No. 3, pp. 461-498.

Lemieux, Thomas (2008). “The Changing Nature of Wage Inequality.” Journal of Population 

Economics, Vol. 21, No. 1, pp. 21-48.

Perun, Pamela, "FAQs about Employees and Employee Benefits" (2010). Georgetown Law 

School Memos and Fact Sheets. Paper 46. Available at 

http://scholarship.law.georgetown.edu/legal/46

Rios-Avila, Fernando, “Recentered Influence Functions (RIF) in Stata: RIF Regression and RIF 

Decomposition” Stata Journal. Forthcoming. 
19

Song, Jae, David J. Price, Fatih Guvenen, Nicholas Bloom, and Till von Wachter. (2016) 

Firming Up Inequality. No. w21199. National Bureau of Economic Research, 2015.

Weil, David. The fissured workplace. Harvard University Press, 2014.
20

Figure 1: Trends in Employment by Quintile of Occupation

Note: The 46,609,394 observations in 29 waves of data are used to calculate overall average 

wage levels and employment levels. These are grouped into quintiles of occupation by average 

wage levels (as shown in Appendix A). This figure shows the percentage of employment in each 

occupational quintile in each wave of OES data, from November 2002 through November 2016.
21

Figure 2: Workers with no coworkers in other occupational quintiles over time in the OES data, 

by quintile of occupation and quintile of coworkers

Note: The 46,609,394 observations in 29 waves of data are used to calculate overall average 

wage levels and employment levels. These are grouped into quintiles of occupations by average 

wage levels (as shown in Appendix A). This figure shows the percentage of workers in each 

quintile who are employed in establishments that have no workers in each other quintile, by 

panel (from November 2002 to November 2016). For example, the subgraph at the top left shows 

the fraction of workers in the lowest-quintile of occupations who have no co-workers in each 

other quintile of occupations, for each panel of the OES data.
22

Figure 3: Trends in Employment by Quintile of Occupation and Size of Employing 

Establishment

Note: The 46,609,394 observations in 29 waves of data are used to calculate overall average 

wage levels and employment levels. These are grouped into quintiles of occupation by average 

wage levels (as shown in Appendix A). This figure shows the percentage of employment in each 

establishment size group in each occupational quintile in each wave of OES data, from 

November 2002 through November 2016.
Table 1: Mean Values of Occupational Homogeneity for Specified Occupations and 

Industries, 2002-2016

Occupation and Industry

Avg 

ln(wage)

Mean Value of Occupational 

Homogeneity

Herfindahl of 

Occupational 

Homogeneity for the 

establishment

Predicted Variance 

of Wages for the 

establishment

Food preparation and serving (SOC 35)

within Food Services (NAICS 722) – 79%

within all other industries – 21%

1.99

2.11

0.496

0.252

0.138

0.245

Janitors (SOC 372011)

within Janitorial Services (NAICS 561720) – 46%

within all other industries – 54%

2.04

2.16

0.842

0.329

0.141

0.267

Security Guards (SOC 339032)

within Security Guard Srvcs (NAICS 561612) – 62%

within all other industries – 38%

2.16

2.32

0.883

0.322

0.158

0.267

Truck Drivers (SOC 53303)

within Truck Transportation (NAICS 484) – 30%

within all other industries – 70%

2.69

2.45

0.636

0.379

0.204

0.248

Accountants (SOC 132011)

within Accounting Services (NAICS 541211) – 25%

within all other industries – 75%

3.22

3.16

0.574

0.285

0.307

0.343

Computer Occupations (SOC 151)

within Computer Services (NAICS 5415) – 27%

within all other industries – 73%

3.34

3.31

0.588

0.302

0.272

0.329

Engineers (SOC 172)

within Engineering Services (NAICS 54133) – 22%

within all other industries – 78%

3.40

3.44

0.401

0.249

0.264

0.309

Lawyers (SOC 231011)

within Law Offices (NAICS 54111) – 84%

within all other industries – 16%

3.76

3.87

0.411

0.277

0.544

0.409
24

Table 2: Change in mean values of Occupational Homogeneity over time

Note: These are coefficients α from regressions of the form Occupation Homogeneityijt =

αSurvey Datet + βI(May Surveyt

) + γXijt + εijt, where the Survey Date is measured in 

decades since Xijt includes occupation fixed effects at the 6-digit SOC level, industry fixed 

effects at the 4-digit NAICS level, establishment size class, continuous establishment size, and 

state. All coefficients are significant at p < 0.001. Regressions are at the establishmentoccupation-wage interval level, weighted by employment. Standard errors in parentheses

Trend regressions for quintiles of unimputed OES private-sector data from Nov 2002-Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity Variable

All Occupations (46,609,394 observations)

Raw trend 0.0057 0.0059

(0.00009) (0.00004)

All Controls 0.0049 0.0048

(0.00006) (0.00003)

Lowest-paid quintile of occupations (4,512,045 observations)

Raw trend 0.0153 -0.0167

(0.00030) (0.00011)

All Controls 0.0050 -0.0123

(0.00020) (0.00008)

Second quintile of occupations (6,742,713 observations)

Raw trend 0.0134 -0.0009

(0.00024) (0.00009)

All Controls 0.0038 0.0032

(0.00016) (0.00007)

Middle quintile of occupations (9,757,141 observations)

Raw trend 0.0073 0.0080

(0.00017) (0.00008)

All Controls 0.0011 0.0099

(0.00013) (0.00006)

Fourth quintile of occupations (11,881,704 observations)

Raw trend -0.0079 0.0140

(0.00018) (0.00006)

All Controls 0.0027 0.0102

(0.00013) (0.00004)

Highest-paid quintile of occupations (13,715,791 observations)

Raw trend 0.0065 0.0187

(0.00014) (0.00006)

All Controls 0.0101 0.0157

(0.00011) (0.00004)

Herfindahl of occupational 

homogeneity for the 

establishment

Predicted Variance 

of Wages for the 

establishment
25

Table 3: Change in mean values of Occupational Homogeneity over time, by establishment 

size

Trend regressions for quintiles of unimputed OES private-sector data from Nov 2002-Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity 

Variable

Herfindahl of 

occupational 

homogeneity for the 

establishment

Predicted 

Variance of 

Wages for the 

establishment

1-99 Employees

All Occupations (27,077,995 observations)

All Controls 0.0091 0.0034

(0.0001) (0.0000)

Lowest-paid quintile of occupations (2,942,146 observations)

All Controls 0.0120 -0.0137

(0.0003) (0.0001)

100+ Employees

All Occupations (19,531,399 observations)

All Controls 0.0035 0.0066

(0.0001) (0.0000)

Lowest-paid quintile of occupations (1,569,899 observations)

All Controls -0.0057 -0.0100

(0.0003) (0.0001)

Note: These are coefficients α from regressions of the form Occupation Homogeneityijt =

αSurvey Datet + βI(May Surveyt

) + γXijt + εijt, where the Survey Date is measured in 

decades since Xijt includes occupation fixed effects at the 6-digit SOC level, industry fixed 

effects at the 4-digit NAICS level, establishment size class, continuous establishment size, and 

state. All coefficients are significant at p < 0.001. Regressions are at the establishmentoccupation-wage interval level, weighted by employment. Standard errors in parentheses
26

Table 4: Regressions of log wages on measures of Occupational Homogeneity 

Notes: These are coefficients  from regressions of the form ln(wageijt) =

αOccHomogeneityjt + βOccConcenjtDatet + γXijt + εijt, where Date is measured in 

decades since November 1, 2002, X includes survey date fixed effects, occupation fixed effects 

at the 6-digit SOC level, industry fixed effects at the 4-digit NAICS level, state fixed effects, and 

establishment size (using fixed effects for establishment size classes as well as continuous 

establishment size). All coefficients are significant at p < 0.001. Regressions are at the 

establishment-occupation-wage interval level, weighted by employment. Standard errors in 

parentheses.

Wage regressions for quintiles of unimputed OES private-sector data from Nov 2002 - Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity Variable

All Occupations (46,609,394 observations)

With only date fixed effects -0.496 2.285

(0.001) (0.002)

All Controls -0.107 0.234

(0.000) (0.001)

Lowest-paid quintile of occupations (4,512,045 observations)

With only date fixed effects -0.229 0.882

(0.001) (0.003)

All Controls -0.145 0.358

(0.001) (0.003)

Second quintile of occupations (6,742,713 observations)

With only date fixed effects -0.254 0.722

(0.001) (0.003)

All Controls -0.159 0.377

(0.001) (0.002)

Middle quintile of occupations (9,757,141 observations)

With only date fixed effects -0.199 0.579

(0.001) (0.002)

All Controls -0.122 0.295

(0.001) (0.002)

Fourth quintile of occupations (11,881,704 observations)

With only date fixed effects -0.249 0.647

(0.001) (0.003)

All Controls -0.074 0.096

(0.001) (0.003)

Highest-paid quintile of occupations (13,715,791 observations)

With only date fixed effects -0.206 0.463

(0.001) (0.003)

All Controls 0.009 -0.153

(0.001) (0.003)

Herfindahl of occupational 

homogeneity for the 

establishment

Predicted Variance 

of Wages for the 

establishment
27

Table 5: Decomposition of Changes in real ln(wage) variance from 2002-2003 to 2014-2015 

Notes: These are the results of Rios-Avila’s implementation of Fortin, Firpo, and Lemiueux’s 

Recentered Influence Function Decomposition. Industry here is grouped into 19 supersectors and 

geography into 7 Census divisions. Establishment size is measured in 8 categories (1-4 

employees, 5-9, 10-19, 20-49, 50-99, 100-249, 250-499, and 500+). Establishment-level

Herfindahl-Hirschman indices of occupations are measured with quartiles of the distribution, 

interacted with occupational quintiles. The predicted variance of ln(wage) for establishments is

divided into quartiles, and also interacted with occupational quintiles, with an additional dummy 

variable for low-wage occupations in establishment of less than 100 workers that are in the 

bottom half of the predicted variance distribution. Standard deviations are the result of 

bootstrapping the coefficients with 300 replications. Additional specifications are shown in 

Appendix C and Appendix D.

real log wage variance Coeff. Percent Bootstrap Std. Dev.

Overall Variance

Late period (2014-2015) 0.3999 0.0007

counterfactual variance 0.3724 0.0007

Early period (2002-2003) 0.3754 0.0012

Total change 0.0245 100% 0.0013

of which explained (by compositional change) 0.0274 112% 0.0006

of which unexplained (wage structure change) -0.0030 -12% 0.0011

Explained (compositional effect)

Total 0.0274 100% 0.0006

Pure_explained 0.0268 98% 0.0006

Specification_error 0.0006 2% 0.0001

Components of the pure explained effect

industrial sector -0.0002 -1% 0.0002

geography (Census division) -0.0001 0% 0.0001

size 0.0005 2% 0.0001

quintiles of occupation 0.0239 87% 0.0007

Establishment Herfindahls -0.0002 -1% 0.0003

Establishment predicted var(ln wages) 0.0029 11% 0.0003

Unexplained (wage structure changes) 0.0000

Total -0.0030 100% 0.0011

Reweight_error -0.0001 4% 0.0001

Pure_Unexplained -0.0029 96% 0.0011
Data Appendix

This paper uses Occupational Employment Statistics (OES) Survey microdata. The OES

survey is designed to measure occupational employment and wages in the United States by 

geography and industry, and is the only such survey of its size and scope, covering all 

establishments in the United States except those in agriculture, private households, and 

unincorporated self-employed workers without employees. Every year, approximately 400,000 

private and local government establishments are asked to report the number of employees in each 

occupation paid within specific wage intervals: 200,000 establishments each November and 

another 200,000 each May. As described in Dey and Handwerker, the OES uses a complex

sample design intended to minimize the variance of wage estimates for each occupation within 

industries and geographic areas. Thus, establishments expected to employ occupations with 

greater variation in wages have relatively larger probabilities of selection and lower estimation 

weights.

The OES survey form is a matrix of detailed occupations and wage intervals. For large 

establishments, the survey form lists 50 to 225 detailed occupations; these occupations preprinted on the survey form are selected based on the industry and the size of the establishment.

Small establishments write descriptions of the work done by their employees, which are coded 

into occupations by staff in state labor agencies. Wage intervals on the OES survey form are 

given in both hourly and annual nominal dollars, with annual earnings that are 2080 times the 

hourly wage rates. To calculate average wages, the OES program obtains the mean of each wage 

interval every year from the National Compensation Survey (NCS). These mean wages are then 

assigned to all employees in that wage interval. The OES survey is not designed to produce time 

series statistics. Time series in this paper are produced using the methodology described in 

Abraham and Spletzer (2010) to reweight the data to November or May benchmarks of total 

employment by detailed industry and by broad industry and establishment size groups from the 

Quarterly Census of Employment and Wages (QCEW).

The OES has been using the Standard Occupational Classification System since 1999, 

and had a change of industry classification systems from SIC to NAICS (2002) soon thereafter.

Certain SOC and NAICS codes are combined to make groups consistent across the 2007 and 

2012 NAICS revisions and the 2010 revision to the SOC. Data used in this paper begin in 2002 

to avoid inconsistencies of SOC coding in small establishments during the initial years that the 

OES program used this coding system, as described by Abraham and Spletzer (2010).

Handwerker and Spletzer (2016) examine the decomposition of total wage variance in the 

OES into its within-establishment and between establishment components at length. Updating 

their findings, over the period of Fall 1999 through November 2016, 60% of wage variance is 

between establishments, while all of the growth in overall wage variance over this period is 

between establishments. Handwerker and Spletzer (2016) also find that similar amounts of 

establishment-level wage variance in the OES can be explained by broad industry groups to the 

amount found by Barth, Bryson, Davis, and Freeman. However, more of the establishment-level 

wage variance can be explained by detailed industry in the OES data than in the Census data, 

echoing findings comparing OES and CPS data.
29

Data Appendix Table 1: Summary Statistics

Variable description

Occupation by 

wage interval 

observations

Employment 

represented

Weighted 

Mean Minimum Maximum Variance

OES wages, by establishment-occupation-wage interval 46,609,428 2,242,528,409 20.043 5.778 145.759 310.785

OES real wages, by estab-occupation-wage interval 46,609,428 2,242,528,409 16.203 5.209 106.443 197.447

OES real ln wage, by estab-occupation-wage interval 46,609,428 2,242,528,409 2.561 1.650 4.668 0.382

measured var(ln(wg)) of establishment 46,609,428 2,242,528,409 0.154 0.000 2.091 0.019

Herfindahl of 3-digit occupations, establishment level 46,609,428 2,242,528,409 0.408 0.032 1.000 0.063

pred var(ln(wg)) for establishment, based on occupations 46,609,394 2,242,528,409 0.270 0.018 1.016 0.012

portion of above due to variation between occupations 46,609,394 2,242,528,409 0.106 0.000 0.781 0.006

predicted mean(ln(wg)) of estab, based on occupations 46,609,394 2,242,528,409 2.572 1.807 3.851 0.113

Total employment of establishment 46,609,428 2,242,528,409 576 1 56,473 5,104,405

Date of observation 46,609,428 2,242,528,409 Nov, 2009 Nov, 2002 Nov, 2016

Variable description

Occupation by 

wage interval 

observations

Employment 

represented

Fraction of 

employment

Establishment 

observations

Quintiles

Bottom quintile of occupations 4,512,048 498,610,022 22.2% 1,298,634

Second quintile of occupations 6,742,720 404,179,707 18.0% 1,946,156

Third quintile of occupations 9,757,150 434,006,537 19.4% 2,515,660

Fourth quintile of occupations 11,881,718 486,554,202 21.7% 2,806,055

Top quintile of occupations 13,715,792 419,177,942 18.7% 2,335,591

Industries (2-digit)

Agriculture, Forestry, Fishing and Hunting 24,742 1,209,745 0.1% 5,064

Mining, Quarrying, and Oil and Gas Extraction 278,611 10,731,557 0.5% 22,949

Utilities 362,131 11,487,328 0.5% 20,980

Construction 2,715,765 140,698,009 6.3% 320,008

Manufacturing 7,617,106 247,417,992 11.0% 404,392

Wholesale Trade 3,121,983 106,037,580 4.7% 289,829

Retail Trade 6,033,799 344,932,292 15.4% 502,403

Transportation and Warehousing 1,372,852 92,123,403 4.1% 142,832

Information 1,509,864 49,886,150 2.2% 110,529

Finance and Insurance 2,691,456 118,883,973 5.3% 227,072

Real Estate and Rental and Leasing 673,419 28,956,088 1.3% 99,687

Professional, Scientific, and Technical Services 3,514,547 143,457,542 6.4% 354,483

Management of Companies and Enterprises 1,276,951 32,276,040 1.4% 38,583

Administrative and Support and Waste Management and Remediation Services 2,291,647 140,897,584 6.3% 246,780

Educational Services 1,298,489 50,396,572 2.2% 70,057

Health Care and Social Assistance 6,809,349 353,872,634 15.8% 420,820

Arts, Entertainment, and Recreation 1,062,174 37,963,004 1.7% 96,342

Accommodation and Food Services 2,078,374 237,573,956 10.6% 209,645

Other Services (except Public Administration) 1,876,169 93,726,962 4.2% 282,115

Occupations (2-digit)

Management Occupations 5,121,383 103,271,611 4.6%

Business and Financial Operations Occupations 3,695,454 99,137,164 4.4%

Computer and Mathematical Occupations 1,705,143 53,350,145 2.4%

Architecture and Engineering Occupations 1,210,073 39,732,902 1.8%

Life, Physical, and Social Science Occupations 347,605 12,029,141 0.5%

Community and Social Service Occupations 611,383 23,933,901 1.1%

Legal Occupations 249,027 16,324,258 0.7%

Education, Training, and Library Occupations 654,815 38,966,650 1.7%

Arts, Design, Entertainment, Sports, and Media Occupations 981,616 28,832,089 1.3%

Healthcare Practitioners and Technical Occupations 2,195,949 134,205,129 6.0%

Healthcare Support Occupations 732,683 75,368,512 3.4%

Protective Service Occupations 322,367 23,877,442 1.1%

Food Preparation and Serving Related Occupations 1,918,555 223,615,488 10.0%

Building and Grounds Cleaning and Maintenance 1,125,051 72,733,714 3.2%

Personal Care and Service Occupations 800,258 73,439,546 3.3%

Sales and Related Occupations 4,545,163 297,008,580 13.2%

Office and Administrative Support Occupations 10,417,328 375,746,893 16.8%

Farming, Fishing, and Forestry Occupations 63,893 3,315,741 0.1%

Construction and Extraction Occupations 1,546,401 108,571,047 4.8%

Installation, Maintenance, and Repair Occupations 2,384,025 97,043,195 4.3%

Production Occupations 3,467,582 174,526,262 7.8%

Transportation and Material Moving Occupations 2,513,674 167,498,997 7.5%
30

Appendix A: Occupations by Quintile

3-digit 

SOC 

code SOC Title

Average 

ln(wage)

Cummulative 

percentage of 

employment

Occupation 

Quintile

359 Other Food Preparation and Serving Related Workers 1.94 1.1% 1

353 Food and Beverage Serving Workers 1.94 6.5% 1

393 Entertainment Attendants and Related Workers 1.98 6.9% 1

352 Cooks and Food Preparation Workers 2.05 9.3% 1

392 Animal Care and Service Workers 2.09 9.4% 1

399 Other Personal Care and Service Workers 2.09 11.1% 1

412 Retail Sales Workers 2.10 18.6% 1

372 Building Cleaning and Pest Control Workers 2.10 20.9% 1

452 Agricultural Workers 2.11 21.0% 2

536 Other Transportation Workers 2.13 21.2% 2

516 Textile Apparel and Furnishings Workers 2.15 21.9% 2

311 Nursing Psychiatric and Home Health Aides 2.16 23.8% 2

396 Baggage Porters Bellhops and Concierges 2.19 23.9% 2

395 Personal Appearance Workers 2.20 24.3% 2

373 Grounds Maintenance Workers 2.21 25.0% 2

339 Other Protective Service Workers 2.23 26.0% 2

397 Tour and Travel Guides 2.23 26.0% 2

513 Food Processing Workers 2.24 26.7% 2

537 Material Moving Workers 2.24 30.6% 2

379

Other Buildings, Grounds, and Maintenance 

Occupations 2.27 30.6% 2

259 Other Education Training and Library Occupations 2.28 30.9% 2

435 Material Recording Scheduling Dispatching and 2.29 33.8% 2

432 Communications Equipment Operators 2.29 34.0% 2

473 Helpers Construction Trades 2.31 34.2% 2

459 Other Farming, Fishing, and Forestry Occupations 2.32 34.2% 2

453 Fishing and Hunting Workers 2.33 34.2% 2

439 Other Office and Administrative Support Workers 2.35 37.3% 2

517 Woodworkers 2.36 37.5% 2

512 Assemblers and Fabricators 2.40 39.2% 2

434 Information and Record Clerks 2.40 43.5% 3

519 Other Production Occupations 2.40 45.9% 3

319 Other Healthcare Support Occupations 2.42 47.0% 3

351 Supervisors of Food Preparation and Serving Workers 2.45 47.7% 3

433 Financial Clerks 2.48 50.5% 3

332 Fire Fighting and Prevention Workers 2.48 50.5% 3

533 Motor Vehicle Operators 2.49 53.5% 3

454 Forest Conservation and Logging Workers 2.50 53.5% 3

515 Printing Workers 2.53 53.8% 3

219 Other Community and Social Service Occupations 2.53 53.8% 3
31

3-digit 

SOC 

code SOC Title

Average 

ln(wage)

Cummulative 

percentage of 

employment

Occupation 

Quintile

252 Preschool Primary Secondary and Special Education 2.54 54.4% 3

514 Metal Workers and Plastic Workers 2.56 56.2% 3

394 Funeral Service Workers 2.56 56.3% 3

436 Secretaries and Administrative Assistants 2.57 59.0% 3

419 Other Sales and Related Workers 2.57 59.8% 4

253 Other Teachers and Instructors 2.58 60.1% 4

333 Law Enforcement Workers 2.58 60.1% 4

391 Supervisors of Personal Care and Service Workers 2.59 60.2% 4

211 Counselors Social Workers and Other Community and 2.59 61.2% 4

371 Supervisors of Building and Grounds Cleaning and 2.62 61.4% 4

493 Vehicle and Mobile Equipment Mechanics Installers 2.63 62.6% 4

312

Occupational Therapy and Physical Therapist 

Assistants 2.65 62.8% 4

499

Other Installation Maintenance and Repair 

Occupations 2.65 64.9% 4

212 Religious Workers 2.66 64.9% 4

534 Rail Transportation Workers 2.67 65.0% 4

475 Extraction Workers 2.68 65.1% 4

474 Other Construction and Related Workers 2.70 65.3% 4

292 Health Technologists and Technicians 2.71 67.4% 4

472 Construction Trades Workers 2.71 71.1% 4

274 Media and Communication Equipment Workers 2.72 71.2% 4

271 Art and Design Workers 2.77 71.7% 4

331 Supervisors of Protective Service Workers 2.77 71.7% 4

194 Life Physical and Social Science Technicians 2.77 71.9% 4

411 Supervisors of Sales Workers 2.78 73.2% 4

254 Librarians Curators and Archivists 2.79 73.2% 4

272 Entertainers and Performers Sports and Related 2.80 73.6% 4

451 Supervisors of Farming Fishing and Forestry Workers 2.81 73.6% 4

492 Electrical and Electronic Equipment Mechanics Install 2.81 74.1% 4

232 Legal Support Workers 2.83 74.4% 4

531 Supervisors of Transportation and Material Moving 2.87 74.7% 4

239 Other Legal Occupations 2.88 74.7% 4

431 Supervisors of Office and Administrative Support 2.90 75.8% 4

535 Water Transportation Workers 2.90 75.8% 4

299 Other Healthcare Practitioners and Technical Occs 2.91 75.9% 4

173 Drafters Engineering Technicians and Mapping 2.91 76.5% 4

273 Media and Communication Workers 2.93 77.0% 4

511 Supervisors of Production Workers 2.98 77.5% 4

413 Sales Representatives Services 3.01 78.9% 4

518 Plant and System Operators 3.01 79.0% 4

153 All other Computer and Math Occupations 3.05 79.0% 4
32

3-digit 

SOC 

code SOC Title

Average 

ln(wage)

Cummulative 

percentage of 

employment

Occupation 

Quintile

414 Sales Representatives Wholesale and Manufacturing 3.08 80.7% 4

491 Supervisors of Installation Maintenance and Repair 3.08 81.0% 5

131 Business Operations Specialists 3.11 83.8% 5

471 Supervisors of Construction and Extraction Workers 3.11 84.2% 5

193 Social Scientists and Related Workers 3.16 84.3% 5

171 Architects Surveyors and Cartographers 3.17 84.4% 5

132 Financial Specialists 3.17 86.3% 5

251 Postsecondary Teachers 3.18 86.8% 5

159 Computer and Math Occupations, NEC 3.24 86.8% 5

532 Air Transportation Workers 3.26 87.0% 5

192 Physical Scientists 3.31 87.2% 5

151 Computer Specialists 3.32 89.8% 5

191 Life Scientists 3.32 90.0% 5

119 Other Management Occupations 3.33 91.3% 5

152 Mathematical Science Occupations 3.35 91.4% 5

291 Health Diagnosing and Treating Practitioners 3.39 94.8% 5

172 Engineers 3.43 96.0% 5

113 Operations Specialties Managers 3.60 97.2% 5

111 Top Executives 3.64 99.0% 5

112 Advertising Marketing Promotions Public Relations & 3.65 99.6% 5

231 Lawyers Judges and Related Workers 3.76 100.0% 5
33

Appendix B: Heterogeneity of Wage and Trend Results

B1: Heterogeneity by state-level unionization rates

One factor which may impact both wages and the organization of production in terms of the 

variety of occupations at a workplace is unionization. The OES does not collect information on 

unionization patterns by employer, but it includes location of each establishment, and 

unionization rates vary strongly by state. Thus, state-level unionization rates are used to group 

the data into highly unionized states (17-26% of employed workers unionized), middle, and low

unionized states (3-9.3% unionized), based on published tables from the Current Population 

Survey.

Overall, workers in states with higher unionization levels work in slightly (but statistically 

significantly) less occupationally homogeneous establishments. However, for workers in the 

lowest-paid quintile of occupations, this reverses; these workers have slightly higher 

occupational homogeneity in establishments located in states with higher unionization levels.

Differences in occupational homogeneity trends between less and more unionized states 

show that establishments are growing more occupationally homogeneous over time in the lessunionized states, relative to the highly unionized states, by every measure. Following equation 

(3), occupational homogeneity as measured by the Herfindahl-Hirschman index appears to be 

increasing slightly (but significantly) faster in less-unionized states than in highly unionized 

states, both across all occupations and for occupations in the bottom quintile, and so workers in 

less unionized states now work in establishments with higher Herfindahl-Hirschman indices, on 

average, than workers in more unionized states. Occupational homogeneity as measured by the 

predicted variance of ln(wages) based on the occupational composition of establishments, and its 

between-occupations component also show less of an increase in the less unionized states, and 

after employer characteristics and occupation controls are included in equation (3), all measures 

of occupational homogeneity show statistically significant trends of increasing employer 

homogeneity in less unionized states. Differences in trends are similar for the lowest-paid 

quintile of occupations. 

Following equation (4), the relationships between occupational homogeneity and wages are 

estimated separately for each unionization group of states. Across all occupations, the 

relationships α between occupational homogeneity (by all measures) and wages is significantly 

greater in the more highly unionized states. However, this reverses when establishment 

characteristics and occupational controls are included in equation (2), and after including these 

controls, the relationship between occupational homogeneity and wages (α) is significantly 

greater in the less unionized states than in the more highly unionized states. For workers in the 

lowest-paid quintile of occupations, occupational homogeneity (by all measures) matters more 

for wages in less unionized states both with and without controlling for establishment 

characteristics and occupation. Across all workers, time trends interacted with occupational 

homogeneity (β) have varying signs across measures of occupational homogeneity and the 

inclusion of controls, but for the workers in the lowest-paid quintile of occupations, these 

interactions are always significantly lower in less unionized states, indicating that the 
34

relationships between occupational homogeneity and wages for these workers are converging 

over time between the different groups of states.

Table B1: Change in mean values of Occupational Homogeneity over time, by Unionization 

group

Note: These are coefficients α from regressions of the form Occupational Homogeneityijt =

αSurvey Datet + βI(May Surveyt

) + γXijt + εijt, where the Survey Date is measured in 

decades since Xijt includes occupation fixed effects at the 6-digit SOC level, industry fixed 

effects at the 4-digit NAICS level, establishment size class, continuous establishment size, and 

state. Regressions are at the establishment-occupation-wage interval level, weighted by 

employment. Standard errors in parentheses. Regressions at the establishment-occupation-wage 

interval level, weighted by employment.

Highly unionized states

All Occupations (15,699,964 observations)

Raw trend 0.0039 0.0096 

(0.0002) (0.0001)

All Controls 0.0060 0.0086 

(0.0001) (0.0000)

Lowest-paid quintile of occupations (1,494,981 observations)

Raw trend 0.0095 (0.0135)

(0.0005) (0.0002)

All Controls 0.0042 (0.0085)

(0.0003) (0.0001)

Less unionized states

All Occupations (16,120,909 observations)

Raw trend 0.0097 0.0004 

(0.0001) (0.0001)

All Controls 0.0090 (0.0012)

(0.0001) (0.0000)

Lowest-paid quintile of occupations (1,579,134 observations)

Raw trend 0.0186 (0.0230)

(0.0005) (0.0002)

All Controls 0.0131 (0.0197)

(0.0003) (0.0001)

Herfindahl of occupational 

homogeneity for the 

establishment

Predicted Variance of 

Wages for the 

establishment

Trend regressions for quintiles of unimputed OES private-sector data from Nov 2002-Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity 

Variable
35

Table B2: Regressions of log wages on measures of Occupational Homogeneity, by 

Unionization group 

Notes: These are coefficients  from regressions of the form ln(wageijt) =

αOccupational Homogeneityjt + βOccConcenjtDatet + γXijt + εijt, where Date is 

measured in decades since November 1, 2002, X includes survey date fixed effects, occupation 

fixed effects at the 6-digit SOC level, industry fixed effects at the 4-digit NAICS level, state 

fixed effects, and establishment size (using fixed effects for establishment size classes as well as 

continuous establishment size). All coefficients are significant at p < 0.001. Regressions are at 

the establishment-occupation-wage interval level, weighted by employment. Standard errors in 

parentheses.

Highly unionized states

All Occupations (15,699,964 observations)

With only date fixed effects -0.494 2.326

(0.001) (0.003)

All Controls -0.102 0.241

(0.001) (0.002)

Lowest-paid quintile of occupations (1,494,981 observations)

With only date fixed effects -0.186 0.817

(0.002) (0.005)

All Controls -0.125 0.338

(0.002) (0.005)

Less unionized states

All Occupations (16,120,909 observations)

With only date fixed effects -0.498 2.198

(0.001) (0.003)

All Controls -0.134 0.260

(0.001) (0.002)

Lowest-paid quintile of occupations (1,579,134 observations)

With only date fixed effects -0.248 0.895

(0.002) (0.005)

All Controls -0.166 0.377

(0.002) (0.004)

Herfindahl of occupational 

homogeneity for the 

establishment

Predicted Variance of 

Wages for the 

establishment

Wage regressions for quintiles of unimputed OES private-sector data from Nov 2002 - Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity Variable
36

B2: Heterogeneity by establishment age

Because the Occupational Establishment Survey data is sampled from the records of the BLS 

Quarterly Census of Employment and Wages, which BLS assembles into a longitudinal database 

of establishments, it is straightforward to link these datasets together and find a “birth date”—the 

first quarter with employment greater than zero—for each establishment. Dividing 

establishments into those born before the fourth quarter of 2002 (87% of all establishments 

observed) and those born afterwards (13%), the analyses above can be repeated separately for 

“old” and “young” establishments.

Young establishments are, in employment-weighted averages, more homogeneous in 

occupations than old establishments, with higher Herfindahl indices of occupational 

homogeneity (.511 for young establishments compared with .386 for old establishments), and 

lower predicted variances of wages based on the occupational composition of establishments 

(.241 for young establishments compared with .276 for old establishments), with much of the 

difference due to the between-occupations component of this variance (.081 for young 

establishments compared with .111 for old establishments). This pattern is echoed at higher 

levels of employer homogeneity for the establishments of workers in the lowest-paid quintile of 

occupations—for these workers as well, working in younger establishments means working in 

establishments more occupationally homogeneous, by both measures.

There are no clear patterns in differences in trends in occupational homogeneity by establishment 

age—differences vary greatly by which measure of occupational homogeneity is used, whether 

controls for establishment characteristics are included, and which groups of occupations are 

examined.

Examining the relationships between occupational homogeneity and wages by establishment age, 

occupational homogeneity matters more for wages in old establishments than in young 

establishments than in young establishments. The difference between old and young 

establishments becomes much smaller after additional controls are added to equation 2, but 

occupational homogeneity—by every measure—still matters significantly more in old 

establishments than in young establishments. This is true across all workers as well as for

workers in the lowest-paid quintile of occupations. 
37

Table B3: Change in mean values of Occupational Homogeneity over time, by 

Establishment age group

Note: These are coefficients α from regressions of the form Occupational Homogeneityijt =

αSurvey Datet + βI(May Surveyt

) + γXijt + εijt, where the Survey Date is measured in 

decades since Xijt includes occupation fixed effects at the 6-digit SOC level, industry fixed 

effects at the 4-digit NAICS level, establishment size class, continuous establishment size, and 

state. Regressions are at the establishment-occupation-wage interval level, weighted by 

employment. Standard errors in parentheses Regressions at the establishment-occupation-wage 

interval level, weighted by employment.

Old Establishments

All Occupations (6,119,160 observations)

Raw trend -0.0087 0.0117

(0.00032) (0.00013)

All Controls -0.0025 0.0086

(0.00024) (0.00009)

Lowest-paid quintile of occupations (742,133 observations)

Raw trend 0.0219 -0.0034

(0.00090) (0.00031)

All Controls -0.0003 -0.0020

(0.00062) (0.00026)

Young establishments

All Occupations (39,588,761 observations)

Raw trend -0.0261 0.0150

(0.00009) (0.00004)

All Controls -0.0012 0.0060

(0.00007) (0.00003)

Lowest-paid quintile of occupations (3,659,326 observations)

Raw trend -0.0133 -0.0082

(0.00033) (0.00012)

All Controls 0.0019 -0.0122

(0.00022) (0.00009)

Trend regressions for quintiles of unimputed OES private-sector data from Nov 2002-Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity Variable

Herfindahl of occupational 

homogeneity for the 

establishment

Predicted Variance of 

Wages for the 

establishment
38

Table B4: Regressions of log wages on measures of Occupational Homogeneity, by 

Establishment age group 

Notes: These are coefficients  from regressions of the form ln(wageijt) =

αOccupational Homogeneityjt + βOccConcenjtDatet + γXijt + εijt, where Date is 

measured in decades since November 1, 2002, X includes survey date fixed effects, occupation 

fixed effects at the 6-digit SOC level, industry fixed effects at the 4-digit NAICS level, state 

fixed effects, and establishment size (using fixed effects for establishment size classes as well as 

continuous establishment size). All coefficients are significant at p < 0.001. Regressions are at 

the establishment-occupation-wage interval level, weighted by employment. Standard errors in 

parentheses.

Old Establishments

All Occupations (6,119,160 observations)

With only date fixed effects -0.285 2.330

(0.001) (0.002)

All Controls -0.104 0.167

(0.001) (0.002)

Lowest-paid quintile of occupations (742,133 observations)

With only date fixed effects -0.110 0.469

(0.001) (0.003)

All Controls -0.097 0.187

(0.002) (0.004)

Young establishments

All Occupations (39,588,761 observations)

With only date fixed effects -0.572 2.382

(0.000) (0.001)

All Controls -0.099 0.201

(0.000) (0.001)

Lowest-paid quintile of occupations (3,659,326 observations)

With only date fixed effects -0.234 0.783

(0.001) (0.002)

All Controls -0.131 0.227

(0.001) (0.002)

Wage regressions for quintiles of unimputed OES private-sector data from Nov 2002 - Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity Variable

Herfindahl of occupational 

homogeneity for the 

establishment

Predicted Variance of 

Wages for the 

establishment
39

B3: Heterogeneity by establishment size

Subdividing establishments into the same size classes used in the regression controls (1-4 

employees, 5-9, 10-19, 20-49, 50-99, 100-249, 250-499, and 500+), there is a mostly monotonic 

decrease in employment-weighted means of occupational homogeneity with establishment size, 

by every measure of occupational homogeneity. Larger establishments are more heterogeneous 

in occupations. There is no clear pattern across establishment sizes of relationships between 

occupational homogeneity and wages for all workers. However, for the lowest-paid quintile of 

occupations, the strongest relationships between occupational homogeneity and wages—across 

all measures, with and without controlling for other observable characteristics—appears in 

middle-sized establishments: those with 50-99 employees.

Table B5: Change in mean values of Occupational Homogeneity over time, by 

Establishment size group

Trend regressions for quintiles of unimputed OES private-sector data from Nov 2002-Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity 

Variable

Herfindahl of 

occupational 

homogeneity for the 

establishment

Predicted 

Variance of 

Wages for the 

establishment

1-4 Employees

All Occupations (1,798,937 observations)

All Controls 0.0041 0.0097

(0.0004) (0.0002)

Lowest-paid quintile of occupations (189,669 

observations)

All Controls -0.0034 -0.0125

(0.0013) (0.0005)

5-9 Employees

All Occupations (3,382,494 observations)

All Controls 0.0009 0.0101

(0.0002) (0.0001)

Lowest-paid quintile of occupations (401,487 observations)

All Controls -0.0008 -0.0076

(0.0008) (0.0004)

10-19 Employees

All Occupations (5,381,267 observations)

All Controls 0.0080 0.0008

(0.0002) (0.0001)

Lowest-paid quintile of occupations (605,146 observations)

All Controls 0.0114 -0.0153

(0.0006) (0.0003)
40

Occupational Homogeneity 

Variable

Herfindahl of occupational 

homogeneity 

Predicted Variance 

of Wages 

20-49 Employees

All Occupations (9,048,401 observations)

All Controls 0.0094 -0.0007

(0.0001) (0.0001)

Lowest-paid quintile of occupations (950,061 observations)

All Controls 0.0135 -0.0151

(0.0004) (0.0002)

50-99 Employees

All Occupations (7,466,896 observations)

All Controls 0.0070 0.0030

(0.0001) (0.0001)

Lowest-paid quintile of occupations (795,783 observations)

All Controls 0.0166 -0.0117

(0.0004) (0.0002)

100-249 Employees

All Occupations (8,880,411 observations)

All Controls -0.0037 0.0029

(0.0001) (0.0000)

Lowest-paid quintile of occupations (888,938 observations)

All Controls -0.0071 -0.0159

(0.0004) (0.0001)

250-499 Employees

All Occupations (5,262,100 observations)

All Controls -0.0031 0.0075

(0.0002) (0.0001)

Lowest-paid quintile of occupations (430,509 observations)

All Controls -0.0022 -0.0094

(0.0006) (0.0002)

500+ Employees

All Occupations (5,388,888 observations)

All Controls 0.0129 0.0100

(0.0001) (0.0001)

Lowest-paid quintile of occupations (250,452 observations)

All Controls 0.0034 0.0019

(0.0006) (0.0002)

Note: These are coefficients α from regressions of the form Occupational Homogeneityijt =

αSurvey Datet + βI(May Surveyt

) + γXijt + εijt, where the Survey Date is measured in 

decades since Xijt includes occupation fixed effects at the 6-digit SOC level, industry fixed 

effects at the 4-digit NAICS level, establishment size class, continuous establishment size, and 

state. Regressions are at the establishment-occupation-wage interval level, weighted by 

employment. Standard errors in parentheses Regressions at the establishment-occupation-wage 

interval level, weighted by employment.
41

Table B6: Regressions of log wages on measures of Occupational Homogeneity, by 

Establishment size group 

Wage regressions for quintiles of unimputed OES private-sector data from Nov 2002 - Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity 

Variable

Herfindahl of occupational 

homogeneity for the 

establishment

Predicted Variance of Wages for 

establishment

1-4 Employees

All Occupations (1,798,937 observations)

All Controls -0.064 0.032

(0.002) (0.004)

Lowest-paid quintile of occupations (189,669 observations)

All Controls -0.095 0.115

(0.003) (0.008)

5-9 Employees

All Occupations (3,382,494 observations)

All Controls -0.101 0.071

(0.001) (0.002)

Lowest-paid quintile of occupations (401,487 observations)

All Controls -0.123 0.119

(0.002) (0.004)

10-19 Employees

All Occupations (5,381,267 observations)

All Controls -0.113 0.132

(0.001) (0.002)

Lowest-paid quintile of occupations (605,146 observations)

All Controls -0.125 0.156

(0.002) (0.004)

20-49 Employees

All Occupations (9,048,401 observations)

All Controls -0.107 0.209

(0.001) (0.002)

Lowest-paid quintile of occupations (950,061 observations)

All Controls -0.100 0.208

(0.001) (0.003)

50-99 Employees

All Occupations (7,466,896 observations)

All Controls -0.102 0.327

(0.001) (0.002)

Lowest-paid quintile of occupations (795,783 observations)

All Controls -0.090 0.315

(0.002) (0.005)
42

Occupational Homogeneity Variable

Herfindahl of 

occupational 

homogeneity for the 

establishment

Predicted Variance of 

Wages for the 

establishment

100-249 Employees

All Occupations (8,880,411 observations)

All Controls -0.123 0.406

(0.001) (0.002)

Lowest-paid quintile of occupations (888,938 observations)

All Controls -0.134 0.365

(0.002) (0.005)

250-499 Employees

All Occupations (5,262,100 observations)

All Controls -0.128 0.433

(0.001) (0.002)

Lowest-paid quintile of occupations (430,509 observations)

All Controls -0.131 0.306

(0.003) (0.008)

500+ Employees

All Occupations (5,388,888 observations)

All Controls -0.114 0.345

(0.001) (0.003)

Lowest-paid quintile of occupations (250,452 observations)

All Controls -0.093 0.242

(0.004) (0.011)

Notes: These are coefficients  from regressions of the form ln(wageijt) =

αOccupational Homogeneityjt + βOccConcenjtDatet + γXijt + εijt, where Date is 

measured in decades since November 1, 2002, X includes survey date fixed effects, occupation 

fixed effects at the 6-digit SOC level, industry fixed effects at the 4-digit NAICS level, state 

fixed effects, and establishment size (using fixed effects for establishment size classes as well as 

continuous establishment size). All coefficients are significant at p < 0.001. Regressions are at 

the establishment-occupation-wage interval level, weighted by employment. Standard errors in 

parentheses.
43

B4: Heterogeneity by industrial sector

Dividing establishments into industrial sectors, establishments in the Construction (23), 

Administrative and Support and Waste Management (56), Accommodation and Food Services 

(72), and Other Services (except Public Administration) (81) have high levels of occupational 

homogeneity, by all measures. Management of Companies and Enterprises (55) has particularly 

low levels of occupational homogeneity, by all measures, while Utilities (22) has particularly 

low occupational homogeneity by the Herfindahl-Hirschman measure, and private-sector 

Educational Services (61) has particularly low occupational homogeneity by the total predicated 

variance of wages based on the occupational distribution.

These sectors differ greatly in the fraction of their employment in occupations at different places 

in the wage distribution, but some of the same sectors stand out for having low or high levels of 

occupational homogeneity among employers of workers in occupations in the lowest-paid 

quintile of the labor force. Examining occupational homogeneity levels by sector for workers in 

these low-paid occupations, again, Administrative and Support and Waste Management (56) and 

Other Services (except Public Administration) (81) have particularly high levels of occupational 

homogeneity, by all measures and Management of Companies and Enterprises (55) has 

particularly low levels of occupational homogeneity, while private-sector Educational Services 

(61) has particularly low occupational homogeneity by the total predicated variance of wages 

based on the occupational distribution. However, other sectors with particularly high or low 

levels of occupational homogeneity are different for workers in the bottom-paid quintile of 

occupations. For these workers, establishments in the Educational Services (61) sector also have 

particularly low levels of occupational homogeneity by all measures. Establishments in the 

Finance and Insurance Sector (72) have a high level of occupational homogeneity by the 

Herfindahl measure, while the Accommodation and Food Services (72) and Other Services (42) 

sectors have particularly high levels of occupational homogeneity by both predicted variance 

measures. 

Trends over time in occupational homogeneity vary greatly by sector. Across all occupations and 

all measures of occupational homogeneity, after controlling for observable characteristics, 

occupational homogeneity is increasing within the Accommodation and Food Services (72) and 

Transportation and Warehousing (48) sectors. It is decreasing within the Construction (23), 

(private-sector) Educational Services (61), Manufacturing (31-33), Finance and Insurance (52), 

Real Estate and Rental and Leasing (53), and Other Services (81) sectors, with trends in the 

remaining sectors that vary in direction by measure of occupational homogeneity. For the lowestpaid quintile of occupations, after controlling for observable characteristics, occupational 

homogeneity is increasing by all measures within the Construction (23), Administrative and 

Support and Waste Management (56), Arts, Entertainment, and Recreation (71), and 

Accommodation and Food Services (72) sectors, while it is decreasing by all measures only 

within the Management of Companies and Enterprises (55) and (private-sector) Educational 

Services (61) sectors.

The relationship between occupational homogeneity and wages also varies tremendously by 

sector, even after controlling for the occupations employed within each sector. After controlling 
44

for occupations, detailed industries, state, and establishment size, by every measure, greater 

occupational homogeneity is associated with lower wages within the Construction (23), Retail 

Trade (44), Transportation and Warehousing (48), Real Estate and Rental and Leasing (53), 

Professional, Scientific, and Tech Services (54), Management of Companies and Enterprises 

(55), Administrative and Support and Waste Management (56), Health Care and Social 

Assistance (62), Arts, Entertainment, and Recreation (71), Accommodation and Food Services 

(72), and Other Services (81) sectors. These relationships are particularly strong in the 

Transportation and Warehousing (48) sector. Other sectors have overall relationships between 

occupational homogeneity and wages that vary by measure of occupational homogeneity. For 

bottom-quintile workers, after controlling for observable characteristics, greater occupational 

homogeneity—by every measure—is associated with lower wages within all of the above 

sectors, as well as the Wholesale Trade (42) and (private-sector) Educational Services (61) 

sectors. For typically low-wage workers, the relationship between occupational homogeneity and 

wages is particularly strong across all measures of occupational homogeneity within the Utilities 

(22) and Arts, Entertainment, and Recreation (71) sectors.
45

Table B7: Change in mean values of Occupational Homogeneity over time, by sector

Establishments in the Construction Sector

All Occupations (2,715,765 observations) and all available controls

All Controls -0.0069 0.0091

(0.0003) (0.0001)

Lowest-paid quintile of occupations (18,771 observations) and all available controls

All Controls 0.0168 -0.0044

(0.0032) (0.0017)

Establishments in the Manufacturing Sector

All Occupations (7,617,094 observations) and all available controls

All Controls -0.0049 0.0157

(0.0001) (0.0001)

Lowest-paid quintile of occupations (120,306 observations) and all available controls

All Controls -0.0066 -0.0060

(0.0011) (0.0006)

Establishments in the Wholesale Trade Sector

All Occupations (3,121,979 observations) and all available controls

All Controls 0.0020 0.0176

(0.0002) (0.0001)

Lowest-paid quintile of occupations (111,547 observations) and all available controls

All Controls 0.0031 0.0033

(0.0013) (0.0007)

Establishments in the Retail Trade Sector

All Occupations (6,033,799 observations) and all available controls

All Controls 0.0061 -0.0166

(0.0002) (0.0001)

Lowest-paid quintile of occupations (1,631,641 observations) and all available controls

All Controls 0.0008 -0.0224

(0.0003) (0.0001)

Establishments in the Transportation and Warehousing Sector

All Occupations (1,372,852 observations) and all available controls

All Controls 0.0207 -0.0125

(0.0004) (0.0002)

Lowest-paid quintile of occupations (23,087 observations) and all available controls

All Controls 0.0058 0.0021

(0.0030) (0.0014)

Establishments in the Information Sector

All Occupations (1,509,864 observations) and all available controls

All Controls 0.0050 0.0204

(0.0004) (0.0001)

Lowest-paid quintile of occupations (36,871 observations) and all available controls

All Controls 0.0296 -0.0009

(0.0023) (0.0012)

Establishments in the Finance and Insurance Sector

All Occupations (2,466,725 observations) and all available controls

All Controls -0.0053 0.0218

(0.0002) (0.0001)

Lowest-paid quintile of occupations (18,020 observations) and all available controls

All Controls 0.0020 -0.0079

(0.0037) (0.0021)

Establishments in the Real Estate and Rental and Leasing Sector

All Occupations (898,150 observations) and all available controls

All Controls -0.0039 0.0137

(0.0005) (0.0002)

Lowest-paid quintile of occupations (116,261 observations) and all available controls

All Controls -0.0103 -0.0044

(0.0015) (0.0007)

Trend regressions for quintiles of unimputed OES private-sector data from Nov 2002-Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity Variable

Herfindahl of occupational 

homogeneity for the 

establishment

Predicted Variance of 

Wages for the 

establishment
46

Table B7: Change in mean values of Occupational Homogeneity over time, by sector, cont.

Establishments in the Professional, Scientific, and Tech Servics Sector

All Occupations (3,501,795 observations) and all available controls

All Controls 0.0128 0.0160

(0.0002) (0.0001)

Lowest-paid quintile of occupations (31,438 observations) and all available controls

All Controls 0.0037 0.0050

(0.0023) (0.0014)

Establishments in the Management of Companies and Enterprises Sector

All Occupations (1,276,951 observations) and all available controls

All Controls 0.0006 0.0186

(0.0003) (0.0001)

Lowest-paid quintile of occupations (14,398 observations) and all available controls

All Controls -0.0900 0.0485

(0.0047) (0.0025)

Establishments in the Administrative and Support and Waste Mgmnt Sector

All Occupations (2,304,399 observations) and all available controls

All Controls -0.0005 0.0047

(0.0003) (0.0001)

Lowest-paid quintile of occupations (129,160 observations) and all available controls

All Controls 0.0107 -0.0038

(0.0012) (0.0005)

Establishments in the Educational Services Sector

All Occupations (1,298,489 observations) and all available controls

All Controls -0.0045 0.0260

(0.0003) (0.0001)

Lowest-paid quintile of occupations (65,498 observations) and all available controls

All Controls -0.0263 0.0221

(0.0013) (0.0007)

Establishments in the Health Care and Social Assistance Sector

All Occupations (6,809,331 observations) and all available controls

All Controls 0.0125 0.0090

(0.0001) (0.0001)

Lowest-paid quintile of occupations (614,503 observations) and all available controls

All Controls 0.0046 -0.0061

(0.0005) (0.0002)

Establishments in the Arts, Entertainment, and Recreation Sector

All Occupations (1,062,174 observations) and all available controls

All Controls 0.0088 0.0049

(0.0004) (0.0002)

Lowest-paid quintile of occupations (318,623 observations) and all available controls

All Controls 0.0142 -0.0047

(0.0007) (0.0004)

Establishments in the Accommodation and Food Services Sector

All Occupations (2,078,374 observations) and all available controls

All Controls 0.0081 -0.0090

(0.0003) (0.0001)

Lowest-paid quintile of occupations (1,050,145 observations) and all available controls

All Controls 0.0097 -0.0099

(0.0004) (0.0002)

Establishments in the Other Services (except Public Admin) Sector

All Occupations (1,876,169 observations) and all available controls

All Controls -0.0041 0.0078

(0.0003) (0.0001)

Lowest-paid quintile of occupations (206,167 observations) and all available controls

All Controls -0.0094 -0.0010

(0.0010) (0.0005)

Occupational Homogeneity Variable

Herfindahl of occupational 

homogeneity for the 

establishment

Predicted Variance of 

Wages for the 

establishment
47

Table B8: Regressions of log wages on measures of Occupational Homogeneity, by sector

Establishments in the Construction Sector

All Occupations (2,715,765 observations) and all available controls

All Controls -0.008 1.035

(0.011) (0.042)

Lowest-paid quintile of occupations (18,771 observations) and all available controls

All Controls -0.274 1.932

(0.161) (0.316)

Establishments in the Manufacturing Sector

All Occupations (7,617,094 observations) and all available controls

All Controls 0.001 0.118

(0.007) (0.018)

Lowest-paid quintile of occupations (120,306 observations) and all available controls

All Controls -0.276 0.034

(0.044) (0.096)

Establishments in the Wholesale Trade Sector

All Occupations (3,121,979 observations) and all available controls

All Controls 0.064 0.219

(0.012) (0.034)

Lowest-paid quintile of occupations (111,547 observations) and all available controls

All Controls -0.558 0.020

(0.054) (0.120)

Establishments in the Retail Trade Sector

All Occupations (6,033,799 observations) and all available controls

All Controls -0.463 0.092

(0.007) (0.018)

Lowest-paid quintile of occupations (1,631,641 observations) and all available controls

All Controls -0.609 0.205

(0.011) (0.033)

Establishments in the Transportation and Warehousing Sector

All Occupations (1,372,852 observations) and all available controls

All Controls -0.350 1.465

(0.016) (0.028)

Lowest-paid quintile of occupations (23,087 observations) and all available controls

All Controls -0.049 1.121

(0.121) (0.249)

Establishments in the Information Sector

All Occupations (1,509,864 observations) and all available controls

All Controls -0.108 -0.362

(0.017) (0.045)

Lowest-paid quintile of occupations (36,871 observations) and all available controls

All Controls -0.425 0.531

(0.071) (0.145)

Establishments in the Finance and Insurance Sector

All Occupations (2,466,725 observations) and all available controls

All Controls 0.142 0.489

(0.014) (0.040)

Lowest-paid quintile of occupations (18,020 observations) and all available controls

All Controls 0.075 0.033

(0.103) (0.237)

Establishments in the Real Estate and Rental and Leasing Sector

All Occupations (898,150 observations) and all available controls

All Controls -0.282 0.979

(0.019) (0.047)

Lowest-paid quintile of occupations (116,261 observations) and all available controls

All Controls -0.466 0.567

(0.039) (0.095)

Wage regressions for quintiles of unimputed OES private-sector data from Nov 2002 - Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity Variable

Herfindahl of occupational 

homogeneity for the 

establishment

Predicted Variance of 

Wages for the 

establishment
48

Table B8: Regressions of log wages on measures of Occ Homogeneity, by sector continued

Establishments in the Professional, Scientific, and Tech Servics Sector

All Occupations (3,501,795 observations) and all available controls

All Controls -0.016 0.781

(0.011) (0.023)

Lowest-paid quintile of occupations (31,438 observations) and all available controls

All Controls -0.994 0.833

(0.095) (0.185)

Establishments in the Management of Companies and Enterprises Sector

All Occupations (1,276,951 observations) and all available controls

All Controls -0.603 0.453

(0.027) (0.057)

Lowest-paid quintile of occupations (14,398 observations) and all available controls

All Controls -0.091 2.595

(0.127) (0.254)

Establishments in the Administrative and Support and Waste Mgmnt Sector

All Occupations (2,304,399 observations) and all available controls

All Controls -0.156 0.720

(0.009) (0.026)

Lowest-paid quintile of occupations (129,160 observations) and all available controls

All Controls -0.357 0.859

(0.035) (0.101)

Establishments in the Educational Services Sector

All Occupations (1,298,489 observations) and all available controls

All Controls 0.127 -0.188

(0.020) (0.058)

Lowest-paid quintile of occupations (65,498 observations) and all available controls

All Controls -0.379 0.612

(0.059) (0.136)

Establishments in the Health Care and Social Assistance Sector

All Occupations (6,809,331 observations) and all available controls

All Controls -0.127 0.231

(0.006) (0.013)

Lowest-paid quintile of occupations (614,503 observations) and all available controls

All Controls -0.222 1.072

(0.011) (0.029)

Establishments in the Arts, Entertainment, and Recreation Sector

All Occupations (1,062,174 observations) and all available controls

All Controls -0.670 0.495

(0.018) (0.039)

Lowest-paid quintile of occupations (318,623 observations) and all available controls

All Controls -0.787 0.999

(0.027) (0.069)

Establishments in the Accommodation and Food Services Sector

All Occupations (2,078,374 observations) and all available controls

All Controls -0.075 0.698

(0.009) (0.025)

Lowest-paid quintile of occupations (1,050,145 observations) and all available controls

All Controls 0.023 0.348

(0.011) (0.034)

Establishments in the Other Services (except Public Admin) Sector

All Occupations (1,876,169 observations) and all available controls

All Controls -0.145 0.144

(0.011) (0.032)

Lowest-paid quintile of occupations (206,167 observations) and all available controls

All Controls -0.161 0.558

(0.028) (0.075)

Occupational Homogeneity Variable

Herfindahl of occupational 

homogeneity for the 

establishment

Predicted Variance of 

Wages for the 

establishment
49

B5: Heterogeneity by Employer tax Identification Number (EIN) size 

Song et al find very different patterns of inequality growth from 1978 to 2013 for very large 

firms—those with Employer tax Identification Numbers (EINs) with 10,000 or more 

employees—than for smaller firms. They find that for smaller firms, nearly all inequality growth 

is between firms, explained by greater sorting of workers with higher worker fixed effects to 

firms with higher firm fixed effects, while very larger firms see nearly half of inequality growth 

happening within firms, with falling wages for their lowest-paid workers and rising wages for 

their highest-paid workers. The focus of this analysis is the establishment (except in section V) 

because establishments are the sampling units of the OES, and the OES sampling design often 

includes some but not all establishments of multi-establishment companies, particularly when 

there are establishments in geographic areas with fewer establishments available to sample.

However, the OES microdata can be linked with the EIN numbers that these establishments

submit to the unemployment insurance system, as compiled by the Quarterly Census of 

Employment and Wages (QCEW), and the full employment level of each EIN can be calculated 

in each time period using the QCEW data. As discussed in Handwerker and Mason (2013), very 

large firms may use multiple EINs in the unemployment insurance system, and there is no way to 

link together all of the establishments in these data for very large firms without a tremendous 

amount of manual review. It is straightforward to group establishments into those that are part of 

very large EINs (those with 10,000 or more employees) and those that are not part of these large 

EINs, but the reader should be aware that many establishments not part of large EINs are 

nonetheless part of large firms that use smaller EINs in their quarterly reports to the 

unemployment insurance system.14

The reader should also be aware that very large establishments are included in the OES sample 

with certainty every 6 panels,15 and the reweighting used to break apart the 6-panel groups of 

OES waves used for official OES publications into panel-specific microdata results in big swings 

of estimates (of any variable of interest) from one panel to the next for very large employers.

This makes it difficult to measure trends in any variable for these employers. Nonetheless, the 

OES data show that workers in the bottom quintile of occupations were paid more in huge firms 

than in smaller firms during earlier waves of data collection, but this difference disappeared 

around November 2013. This is consistent with the finding of Song et. al. that workers with low 

values of worker fixed effects in very large firms have seen declining wages over time. It is not 

exactly comparable to Song et. al. because those authors use repeated observations of workers 

over time to estimate worker fixed effects, an estimation not possible with the OES data.

However, there is likely a great deal of overlap between workers in typically-low-wage 

occupations and workers with low fixed effects.

 

14 Song et al estimate that 23% of workers are in these very large firms; in the OES data, only 16% of workers are in 

such large firms. Various BLS projects have attempted to find all the EINs associated with particular sets of firms in 

particular time periods. Using all of the available links from these projects to group EINs together, it is possible to 

consider 20% of workers as associated with firms employing 10,000 or more; results using these larger groupings 

are little changed from the EIN-level results discussed in this section.

15 Where the sample design allows it, the OES program also makes an effort to sample establishments of the same 

employer in the same wave of the sample. Thus, the establishments of individual large employers are likely to 

appear in the OES data every six panels.
50

Overall, the establishments of very large employers have lower Herfindahl-Hirschman values 

(.36, weighted by employment) of occupational homogeneity than the establishments of smaller 

employers (.42), and this is especially true for workers in the bottom quintile of occupations (.44 

for very large employers and .50 for smaller employers). However, the predicted variance of 

wages based on occupational distributions is greater for the establishments of smaller employers 

(.27) than for very large employers (.26), although this reverses for workers in the bottom 

quintile (.20 for smaller employers and .21 for very large employers). The establishments of very 

large employers are much less likely than the establishments of smaller employers to have no 

workers in the top quintile of occupations (13% of the employees of very large employers have 

no top-quintile co-workers, compared with 25% of the employees of smaller establishments), and 

this is particularly true for workers in the bottom quintile (25% of the bottom-quintile employees 

of very large employers have no top-quintile co-workers, compared with 46% of the bottomquintile employees of smaller employers). Relative to smaller employers, very large employers 

have establishments that are more diverse in the groups of occupations they employ, but are 

more homogeneous in the wage range of the occupations they employ—but the very large 

employers of workers of the bottom quintile of occupations workers are a little less 

homogeneous in the wage range of occupations they employ than smaller employers of these 

low-paid occupations.

Trends in occupational homogeneity differ between very large employers and smaller employers 

for smaller employers, for each measure and for all occupations as well as for the lowest-paid 

quintile of occupations, with and without controlling for other observable variables. By all 

measures, equation (3) shows small but significant increases in occupational homogeneity within 

huge employers for every measure of occupational homogeneity. This is the opposite of the trend 

found for small employers for the predicted variance of wages (and its between-establishment 

component) measures.

Overall, occupational homogeneity matters much more for wages within huge employers, by 

every measure of occupational homogeneity. However, this difference reverses once controls for 

establishment characteristics and occupations are added to equation (4), or if the occupations in 

the lowest-paid quintile are examined separately.
51

Table B9: Change in mean values of Occupational Homogeneity over time, by EIN size

Note: These are coefficients α from regressions of the form Occupational Homogeneityijt =

αSurvey Datet + βI(May Surveyt

) + γXijt + εijt, where the Survey Date is measured in 

decades since Xijt includes occupation fixed effects at the 6-digit SOC level, industry fixed 

effects at the 4-digit NAICS level, establishment size class, continuous establishment size, and 

state. Regressions are at the establishment-occupation-wage interval level, weighted by 

employment. Standard errors in parentheses Regressions at the establishment-occupation-wage 

interval level, weighted by employment.

Establishments associated with huge (10,000+) employment EINs

All Occupations (6,028,858 observations)

Raw trend 0.0116 -0.0032

(0.00021) (0.00010)

All Controls 0.0109 -0.0062

(0.00015) (0.00006)

Lowest-paid quintile of occupations (928,376 observations)

Raw trend 0.0168 -0.0236

(0.00055) (0.00019)

All Controls 0.0127 -0.0211

(0.00039) (0.00014)

Establishments associated with smaller EINs

All Occupations (40,580,536 observations)

Raw trend 0.0064 0.0079

(0.00010) (0.00004)

All Controls 0.0071 0.0065

(0.00008) (0.00003)

Lowest-paid quintile of occupations (3,583,669 observations)

Raw trend 0.0169 -0.0152

(0.00034) (0.00013)

All Controls 0.0085 -0.0108

(0.00025) (0.00010)

Trend regressions for quintiles of unimputed OES private-sector data from Nov 2002-Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity Variable

Herfindahl of occupational 

homogeneity for the 

establishment

Predicted Variance 

of Wages for the 

establishment
52

Table B10: Regressions of log wages on measures of Occupational Homogeneity, by EIN 

size

Notes: These are coefficients  from regressions of the form ln(wageijt) =

αOccupational Homogeneityjt + βOccConcenjtDatet + γXijt + εijt, where Date is 

measured in decades since November 1, 2002, X includes survey date fixed effects, occupation 

fixed effects at the 6-digit SOC level, industry fixed effects at the 4-digit NAICS level, state 

fixed effects, and establishment size (using fixed effects for establishment size classes as well as 

continuous establishment size). All coefficients are significant at p < 0.001. Regressions are at 

the establishment-occupation-wage interval level, weighted by employment. Standard errors in 

parentheses.

Establishments associated with huge (10,000+) employment EINs

All Occupations (6,028,858 observations)

With only date fixed effects -0.737 2.547

(0.002) (0.005)

All Controls -0.067 0.228

(0.001) (0.003)

Lowest-paid quintile of occupations (928,376 observations)

With only date fixed effects -0.243 0.817

(0.003) (0.008)

All Controls -0.102 0.276

(0.003) (0.009)

Establishments associated with smaller EINs

All Occupations (40,580,536 observations)

With only date fixed effects -0.472 2.252

(0.001) (0.002)

All Controls -0.108 0.242

(0.000) (0.001)

Lowest-paid quintile of occupations (3,583,669 observations)

With only date fixed effects -0.219 0.876

(0.001) (0.003)

All Controls -0.139 0.346

(0.001) (0.003)

Wage regressions for quintiles of unimputed OES private-sector data from Nov 2002 - Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity Variable

Herfindahl of occupational 

homogeneity for the 

establishment

Predicted Variance 

of Wages for the 

establishment
53

Appendix C: Occupational Homogeneity: Establishment or Firm Measures?

Song, Price, Guvenen, Bloom, and von Wachter (2016) argue that the unit of importance for 

wage inequality should be the firm and not the establishment. In thinking about occupational 

homogeneity, some of the reasons for employers to outsource work to other establishments are 

also reasons to outsource work to other employers entirely. It may be more efficient for even 

multi-establishment employers to specialize in particular areas of work. Regulatory incentives 

for multi-establishment employers to specialize in employing workers in a particular part of the 

wage distribution are less clear. ERISA laws define employers as “controlled groups of 

corporations” and “entities under common control” in requiring common levels of pension and 

welfare benefits among most employees in exchange for favorable tax treatment (Perun 2010), 

and the Affordable Care Act of 2010 extended these provisions by requiring common levels of 

health care benefits among most employees of businesses with a common owner. However, as 

Perun notes, “Employers often invent new organizational structures and worker classifications 

designed to limit participation to favored employees... Regulatory authorities in turn develop 

complicated rules and regulations designed to prevent this.”

This paper focuses on measures of occupational homogeneity at the establishment level

because establishments are the sampling units of the OES, and the OES sampling design often 

includes some but not all establishments of multi-establishment companies, particularly when 

there are establishments in geographic areas with fewer establishments available to sample.

However, the OES microdata can be linked with the EIN (tax-ID) numbers that these 

establishments submit to the unemployment insurance system, as compiled by the Quarterly 

Census of Employment and Wages. As discussed extensively in Handwerker and Mason (2013), 

very large firms may use multiple EINs in the unemployment insurance system, and there is no

way to link together all of the establishments in these data for very large firms without a 

tremendous amount of manual review. Thus, while it is straightforward to recalculate measures 

of occupational homogeneity at the EIN level and repeat the analyses above, such EIN-level 

measures are not true firm-level measures.

Using EIN-level measures of occupational homogeneity instead of establishment-level 

measures has remarkably little impact on any of the main results in this paper.16 Trends in EINlevel measures of occupational homogeneity over time are very similar to those for 

establishment-level measures in Table 2, shown in Table C1. However, the increased 

occupational homogeneity of workers in the bottom quintile is significantly larger when 

measured with establishment-level measures of occupational homogeneity than when using the 

EIN-level equivalents. The relationship between EIN-level measures of occupational 

homogeneity and wages, shown in Table C2, is very similar to that shown for establishmentlevel measures in Table 4.

Reweighting the November 2014/May 2015 data to the November 2002/May 2003 

distribution of EIN-level measures of occupational homogeneity also yields very similar results 

to those shown in Table 5, as shown in Table C3. 

 

16 Note that 92% of the EINs in the OES data (containing 64% of weighted employment) contain data from only one 

establishment per data panel. However, trends in occupational homogeneity are very similar for employers with 10 

or more establishments per data panel.
54

Appendix Table C1: Change in mean values of EIN-level Occupational Homogeneity over 

time

Note: These are coefficients α from regressions of the form Occupational Homogeneityijt =

αSurvey Datet + βI(May Surveyt

) + γXijt + εijt, where the Survey Date is measured in 

decades since Xijt includes occupation fixed effects at the 6-digit SOC level, industry fixed 

effects at the 4-digit NAICS level, establishment size class, continuous establishment size, and 

state. “*” indicates p<0.05; “**” indicates p<0.01; and “***” indicates p<0.001. Regressions are 

at the establishment-occupation-wage interval level, weighted by employment. Standard errors in 

parentheses. Regressions at the establishment-occupation-wage interval level, weighted by 

employment.

Trend regressions for quintiles of unimputed OES private-sector data from Nov 2002-Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity Variable

All Occupations (45,689,947 observations)

Raw trend 0.0002 * 0.0075 ***

(0.00009) (0.00004)

All Controls 0.0009 *** 0.0065 ***

(0.00006) (0.00003)

Lowest-paid quintile of occupations (4,394,954 observations)

Raw trend 0.0127 *** -0.0150 ***

(0.00030) (0.00011)

All Controls 0.0026 *** -0.0104 ***

(0.00021) (0.00009)

Second quintile of occuaptions (6,580,804 observations)

Raw trend 0.0101 *** 0.0001

(0.00024) (0.00009)

All Controls 0.0032 *** 0.0044 ***

(0.00016) (0.00007)

Middle quintile of occupations (9,564,315 observations)

Raw trend 0.0014 *** 0.0104 ***

(0.00017) (0.00008)

All Controls -0.0009 *** 0.0116 ***

(0.00013) (0.00006)

Fourth quintile of occupations (11,653,657 observations)

Raw trend -0.0161 *** 0.0158 ***

(0.00018) (0.00006)

All Controls -0.0032 *** 0.0117 ***

(0.00013) (0.00004)

Highest-paid quintile of occupations (13,496,217 observations)

Raw trend -0.0009 *** 0.0202 ***

(0.00014) (0.00006)

All Controls 0.0027 *** 0.0174 ***

(0.00010) (0.00004)

Herfindahl of 

occupational 

homogeneity for the EIN

Predicted Variance 

of Wages for the 

EIN
55

Appendix Table C2: Regressions of log wages on EIN-level measures of Occupational 

Homogeneity

Notes: These are coefficients  from regressions of the form ln(wageijt) =

αOccupational Homogeneityjt + βOccConcenjtDatet + γXijt + εijt, where Date is 

measured in decades since November 1, 2002, X includes survey date fixed effects, occupation 

fixed effects at the 6-digit SOC level, industry fixed effects at the 4-digit NAICS level, state 

fixed effects, and establishment size (using fixed effects for establishment size classes as well as 

continuous establishment size). All coefficients are significant at p < 0.001. Regressions are at 

the establishment-occupation-wage interval level, weighted by employment. Standard errors in 

parentheses.

Wage regressions for quintiles of unimputed OES private-sector data from Nov 2002 - Nov 2016

Occupations are ranked by average wage and classified into quintiles as shown in Appendix A

Occupational Homogeneity Variable

All Occupations (45,689,947 observations)

With only date fixed effects -0.495 2.315

(0.001) (0.002)

All Controls -0.133 0.296

(0.000) (0.001)

Lowest-paid quintile of occupations (4,394,954 observations)

With only date fixed effects -0.229 0.881

(0.001) (0.003)

All Controls -0.141 0.369

(0.001) (0.003)

Second quintile of occupations (6,580,804 observations)

With only date fixed effects -0.286 0.786

(0.001) (0.003)

All Controls -0.202 0.451

(0.001) (0.002)

Middle quintile of occupations (9,564,315 observations)

With only date fixed effects -0.221 0.612

(0.001) (0.002)

All Controls -0.167 0.370

(0.001) (0.002)

Fourth quintile of occupations (11,653,657 observations)

With only date fixed effects -0.295 0.755

(0.001) (0.003)

All Controls -0.108 0.199

(0.001) (0.003)

Highest-paid quintile of occupations (13,496,217 observations)

With only date fixed effects -0.195 0.526

(0.001) (0.003)

All Controls -0.009 -0.093

(0.001) (0.003)

Herfindahl of 

occupational 

homogeneity for the EIN

Predicted Variance 

of Wages for the 

EIN
56

Appendix Table C3: Decomposition of Changes in real ln(wage) variance from 2002-2003 

to 2014-2015, using EIN-level measures of Occupational Homogeneity and employer size

Notes: These are the results of Rios-Avila’s implementation of Fortin, Firpo, and Lemiueux’s 

Recentered Influence Function Decomposition. Industry here is grouped into 19 supersectors and 

geography into 7 Census divisions. EIN size is measured in 8 categories (1-4 employees, 5-9, 10-

19, 20-49, 50-99, 100-249, 250-499, and 500+). EIN-level Herfindahl-Hirschman indices of 

occupations are measured with quartiles of the distribution, interacted with occupational 

quintiles. The predicted variances of ln(wage) for EINs is divided into quartiles, and also 

interacted with occupational quintiles, with an additional dummy variable for low-wage 

occupations in EINs of less than 100 workers that are in the bottom half of the predicted variance 

distribution. Standard deviations are the result of bootstrapping the coefficients 300 times.

real log wage variance Coeff. Percent Bootstrap Std. Dev.

Overall Variance

Late period (2014-2015) 0.3999 0.0007

counterfactual variance 0.3691 0.0007

Early period (2002-2003) 0.3754 0.0012

Total change 0.0245 100% 0.0013

of which explained (by compositional change) 0.0308 126% 0.0006

of which unexplained (wage structure change) -0.0063 -26% 0.0011

Explained (compositional effect)

Total 0.0308 100% 0.0006

Pure_explained 0.0295 96% 0.0006

Specification_error 0.0013 4% 0.0001

Components of the pure explained effect

industrial sector 0.0000 0% 0.0002

geography (Census division) -0.0001 0% 0.0001

size 0.0013 4% 0.0001

quintiles of occupation 0.0235 76% 0.0006

Establishment Herfindahls 0.0008 3% 0.0004

Establishment predicted var(ln wages) 0.0040 13% 0.0003

Unexplained (wage structure changes)

Total -0.0063 100% 0.0011

Reweight_error -0.0001 2% 0.0001

Pure_Unexplained -0.0062 98% 0.0011
57

Appendix D: Alternative RIF Decompositions of the change in real ln(wage) variance over time

This Appendix shows two variations on Table 5: a decomposition of wage variance change 

using greater detail in the explanatory variables, and a decomposition using a later end date.

D1: Greater Detail in the Explanatory Variables

The main RIF decomposition, shown in Table 5, uses much coarser measures of industry, 

occupation, and geography than the regression controls of Tables 2-4. The results of an alternate 

RIF decomposition with detailed measures of industry, occupation, and geography, as well as 

finer categories of occupational homogeneity are shown in Table D1. Here, only 76% of the 

change in ln(wage) variance can be explained by compositional changes in employer and 

employee characteristics. However, of the amount that can be explained, changes in occupation 

continue to be by far the most important category of variables, with employer homogeneity (by 

the predicted variance of wages measure) the second-most important contributor to increasing 

wage variance. Because changes in wage structure explain 24% of the change in ln(wage) 

variance, the decomposition of wage structure is also shown here by category.
58

Table D1: Decomposition of Changes in real ln(wage) variance from 2002-2003 to 2014-

2015, with greater detail used in the explanatory variables

Notes: These are the results of Rios-Avila’s implementation of Fortin, Firpo, and Lemiueux’s 

Recentered Influence Function Decomposition. Industry here is grouped into 280 time-consistent 

4-digit NAICS codes and geography by state. Establishment size is measured in 8 categories (1-4 

employees, 5-9, 10-19, 20-49, 50-99, 100-249, 250-499, and 500+). Establishment-level

Herfindahl-Hirschman indices of occupations are measured with deciles of the distribution, 

interacted with occupational quintiles. The predicted variance of ln(wage) for establishments is 

divided into deciles, and also interacted with occupational quintiles, with an additional dummy 

variable for low-wage occupations in establishment of less than 100 workers that are in the 

bottom half of the predicted variance distribution. Standard errors have not been bootstrapped

(yet).

real log wage variance Coeff. Percent Std. Err. z P>|z|

Overall Variance

Late period (2014-2015) 0.3999 0.0012 323.49 0 0.3975 0.4023

counterfactual variance 0.3812 0.0011 356.2 0 0.3791 0.3833

Early period (2002-2003) 0.3754 0.0016 238.23 0 0.3723 0.3785

Total change 0.0245 100% 0.0020 12.22 0 0.0206 0.0284

of which explained (by compositional change) 0.0186 76% 0.0005 35.05 0 0.0176 0.0197

of which unexplained (wage structure change) 0.0058 24% 0.0019 3.03 0.002 0.0021 0.0096

Explained (compositional effect)

Total 0.0186 100% 0.0005 35.05 0 0.0176 0.0197

Pure_explained 0.0175 94% 0.0005 32.72 0 0.0165 0.0186

Specification_error 0.0011 6% 0.0002 6.78 0 0.0008 0.0014

Components of the pure explained effect

industry (4-digit NAICS) -0.0007 -3% 0.0004 -1.8 0.073 -0.0014 0.0001

geography (state) -0.0013 -7% 0.0002 -8.58 0 -0.0016 -0.0010

size 0.0020 11% 0.0001 17.56 0 0.0018 0.0022

occupation (minor occupational categories) 0.0184 99% 0.0012 15.77 0 0.0161 0.0207

Establishment Herfindahls -0.0034 -18% 0.0010 -3.46 0.001 -0.0053 -0.0015

Establishment predicted var(ln wages) 0.0025 14% 0.0003 8 0 0.0019 0.0031

Unexplained (wage structure changes)

Total 0.0058 100% 0.0019 3.03 0.002 0.0021 0.0096

Reweight_error -0.0011 -18% 0.0010 -1.07 0.284 -0.0030 0.0009

Pure_Unexplained 0.0069 118% 0.0017 4.1 0 0.0036 0.0102

Components of the pure unexplained effect

industry (4-digit NAICS) -0.0697 -1193% 0.0171 -4.07 0 -0.1033 -0.0362

geography (state) 0.0168 287% 0.0118 1.42 0.155 -0.0063 0.0399

size -0.0396 -677% 0.0053 -7.49 0 -0.0499 -0.0292

occupation (minor occupational categories) 0.0952 1629% 0.0162 5.87 0 0.0634 0.1270

Establishment Herfindahls -0.0233 -398% 0.0115 -2.03 0.042 -0.0457 -0.0008

Establishment predicted var(ln wages) -0.0072 -124% 0.0059 -1.22 0.223 -0.0188 0.0044

_cons 0.0347 594% 0.0259 1.34 0.18 -0.0161 0.0855

[95% Conf. Interval]
59

D2: A later end date

The latest panel of data used in this paper is data collected in November 2016. For reasons 

outside the scope of this paper, the overall level of wage variance in the OES microdata was 

higher than trend in November 2002, and lower than trend in November 2016. Thus, there is not 

much increase in wage variance to explain between these end dates, and changes in the 

composition of employers and employees can explain 179% of the change in wage variance. 

However, the very strong role of changes in the composition of the labor force by quintile

(employment polarization), with a secondary role for employer homogeneity by the predicted 

variance of wages based on the occupational composition of establishments remains. 

Table D2: Decomposition of Changes in real ln(wage) variance from Nov 2002 & May 2003 

to May 2016 & Nov 2016

Notes: These are the results of Rios-Avila’s implementation of Fortin, Firpo, and Lemiueux’s 

Recentered Influence Function Decomposition. Industry here is grouped into 19 supersectors and 

geography into 7 Census divisions. Establishment size is measured in 8 categories (1-4 

employees, 5-9, 10-19, 20-49, 50-99, 100-249, 250-499, and 500+). Establishment-level

real log wage variance Coeff. Percent Bootstrap Std. Dev.

Overall Variance

Late period (2016) 0.3920 0.0010

counterfactual variance 0.3623 0.0009

Early period (2002-2003) 0.3754 0.0012

Total change 0.0166 100% 0.0016

of which explained (by compositional change) 0.0297 179% 0.0007

of which unexplained (wage structure change) -0.0131 -79% 0.0013

Explained (compositional effect)

Total 0.0297 100% 0.0007

Pure_explained 0.0296 100% 0.0007

Specification_error 0.0001 0% 0.0001

Components of the pure explained effect

industrial sector 0.0002 1% 0.0002

geography (Census division) 0.0009 3% 0.0001

size 0.0010 3% 0.0001

quintiles of occupation 0.0276 93% 0.0009

Establishment Herfindahls -0.0018 -6% 0.0006

Establishment predicted var(ln wages) 0.0018 6% 0.0003

Unexplained (wage structure changes)

Total -0.0131 100% 0.0013

Reweight_error 0.0001 -1% 0.0001

Pure_Unexplained -0.0132 101% 0.0013
60

Herfindahl-Hirschman indices of occupations are measured with quartiles of the distribution, 

interacted with occupational quintiles. The predicted variance of ln(wage) for establishments is 

divided into quartiles, and also interacted with occupational quintiles, with an additional dummy 

variable for low-wage occupations in establishment of less than 100 workers that are in the 

bottom half of the predicted variance distribution. Standard deviations are the result of 

bootstrapping the coefficients 300 times.